research limitations of qualitative research

Understanding Qualitative and Quantitative Data

• 7 minute read
• August 22, 2024

Written by:

Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science

Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights.

Introduction

In the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts.

This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis.

Read More:   Exploring 5 Statistical Data Analysis Techniques with Real-World Examples

Defining Qualitative Data

Qualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject.

This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups.

Characteristics of Qualitative Data

• Descriptive : Qualitative data provides rich, detailed descriptions of phenomena, allowing researchers to capture the complexity of human experiences.
• Subjective : The interpretation of qualitative data can vary based on the researcher’s perspective, making it inherently subjective.
• Contextual : This type of data is often context-dependent, meaning that the insights gained can be influenced by the environment or situation in which the data was collected.
• Exploratory : Qualitative data is typically used in exploratory research to generate hypotheses or to understand phenomena that are not well understood.

Examples of Qualitative Data

• Interview transcripts that capture participants’ thoughts and feelings.
• Observational notes from field studies.
• Responses to open-ended questions in surveys.
• Personal narratives or case studies that illustrate individual experiences.

Defining Quantitative Data

Quantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data.

Characteristics of Quantitative Data

• Objective : Quantitative data is generally considered more objective than qualitative data, as it relies on measurable values that can be statistically analysed.
• Structured : This type of data is often collected using structured methods such as surveys with closed-ended questions, experiments, or observational checklists.
• Generalizable : Because quantitative data is based on numerical values, findings can often be generalised to larger populations if the sample is representative.
• Statistical Analysis : Quantitative data lends itself to various statistical analyses , allowing researchers to draw conclusions based on numerical evidence.

Examples of Quantitative Data

• Age, height, and weight measurements.
• Survey results with numerical ratings (e.g., satisfaction scores).
• Test scores or academic performance metrics.
• Financial data such as income, expenses, and profit margins.

Key Differences Between Qualitative and Quantitative Data

Understanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions:

When to Use Qualitative Data

Qualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice:

Exploratory Research

When investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses.

Understanding Context

Qualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis.

Gaining Insights into Attitudes and Behaviours

When the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights.

Developing Theories

Qualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook.

When to Use Quantitative Data

Quantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice:

Testing Hypotheses

When researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses.

Measuring Variables

Quantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys.

Generalising Findings

When the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions.

Identifying Patterns and Trends

Quantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development.

Integrating Qualitative and Quantitative Data

While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights.

Benefits of Integration

Integrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.

• Enhanced Validity: By using both qualitative and quantitative data, researchers can validate their findings through multiple sources of evidence.
• Rich Insights : Qualitative data can provide context and depth to quantitative findings, helping to explain the “why” behind numerical trends.
• Comprehensive Understanding: Integrating both types of data allows for a more holistic understanding of complex phenomena, leading to more informed conclusions and recommendations.

Examples of Integration

• Surveys with Open-Ended Questions: Combining closed-ended questions (quantitative) with open-ended questions (qualitative) in surveys can provide both measurable data and rich descriptive insights.
• Case Studies with Statistical Analysis: Researchers can conduct case studies (qualitative) while also collecting quantitative data to support their findings, offering a more robust analysis.
• Focus Groups with Follow-Up Surveys: After conducting focus groups (qualitative), researchers can administer surveys (quantitative) to a larger population to validate the insights gained.

Challenges and Considerations

While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type:

Challenges of Qualitative Data

The challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data:

Subjectivity and Bias

One of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data.

Data Overload

Qualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked.

Lack of Structure

Qualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data.

Time-Consuming Nature

Qualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers.

Challenges of Quantitative Data

Quantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations.

Limits in Capturing Complexity

Quantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours.

Chances for Misinterpretation

Numbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data.

Influence of Measurement Errors

Due to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis.

Lack of Context

Quantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied.

Sample Size Limitations

Small sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations.

Confirmation Bias

Researchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights.

In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives.

By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making.

As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike.

Frequently Asked Questions

What is the primary difference between qualitative and quantitative data.

The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses.

When Should I Use Qualitative Data in My Research?

Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important.

Can Qualitative and Quantitative Data Be Used Together?

Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights.

Reviewed by:

Post written by: Smith Alex

Artificial Intelligence Web Scraping vs. Web Crawling: Understanding the Differences

Python Metaprogramming: Unlocking the Power of Code Manipulation

You may also like.

• Data Analysts

Top Data Analytics Projects in 2024 for Beginners to Experienced

• July 20, 2023

Explaining Four Types of Analytics With Examples

• Shlok Kamat
• July 24, 2023
• 15 minute read

• Open access
• Published: 26 August 2024

Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research

• Elham Nasiri   ORCID: orcid.org/0000-0002-0644-1646 1 &
• Laleh Khojasteh   ORCID: orcid.org/0000-0002-6393-2759 1  

BMC Medical Education volume  24 , Article number:  925 ( 2024 ) Cite this article

15 Accesses

Metrics details

This study investigates the effectiveness of panel discussions, a specific interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, in English for Specific Purposes (ESP) courses for international medical students. This approach aims to simulate professional conference discussions, preparing students for future academic and clinical environments where such skills are crucial. While traditional group presentations foster critical thinking and communication, a gap exists in understanding how medical students perceive the complexities of preparing for and participating in panel discussions within an ESP setting. This qualitative study investigates the perceived advantages and disadvantages of these discussions from the perspectives of both panelists (medical students) and the audience (peers). Additionally, the study explores potential improvements based on insights from ESP instructors. Utilizing a two-phase design involving reflection papers and focus group discussions, data were collected from 46 medical students and three ESP instructors. Thematic analysis revealed that panel discussions offer unique benefits compared to traditional presentations, including enhanced engagement and more dynamic skill development for both panelists and the audience. Panelists reported gains in personal and professional development, including honing critical thinking, communication, and presentation skills. The audience perceived these discussions as engaging learning experiences that fostered critical analysis and information synthesis. However, challenges such as academic workload and concerns about discussion quality were also identified. The study concludes that panel discussions, when implemented effectively, can be a valuable tool for enhancing critical thinking, communication skills, and subject matter knowledge in ESP courses for medical students. These skills are transferable and can benefit students in various academic and professional settings, including future participation in medical conferences. This research provides valuable insights for ESP instructors seeking to integrate panel discussions into their curriculum, ultimately improving student learning outcomes and preparing them for future success in professional communication.

Peer Review reports

Introduction

In the field of medical education, the acquisition and application of effective communication skills are crucial for medical students in today’s global healthcare environment [ 1 ]. This necessitates not only strong English language proficiency but also the ability to present complex medical information clearly and concisely to diverse audiences.

Language courses, especially English for Specific Purposes (ESP) courses for medical students, are highly relevant in today’s globalized healthcare environment [ 2 ]. In non-English speaking countries like Iran, these courses are particularly important as they go beyond mere language instruction to include the development of critical thinking, cultural competence, and professional communication skills [ 3 ]. Proficiency in English is crucial for accessing up-to-date research, participating in international conferences, and communicating with patients and colleagues from diverse backgrounds [ 4 ]. Additionally, ESP courses help medical students understand and use medical terminologies accurately, which is essential for reading technical articles, listening to audio presentations, and giving spoken presentations [ 5 ]. In countries where English is not the primary language, ESP courses ensure that medical professionals can stay current with global advancements and collaborate effectively on an international scale [ 6 ]. Furthermore, these courses support students who may seek to practice medicine abroad, enhancing their career opportunities and professional growth [ 7 ].

Moreover, ESP courses enable medical professionals to communicate effectively with international patients, which is crucial in multicultural societies and for medical tourism, ensuring that patient care is not compromised due to language barriers [ 8 ]. Many medical textbooks, journals, and online resources are available primarily in English, and ESP courses equip medical students with the necessary language skills to access and comprehend these resources, ensuring they are well-informed about the latest medical research and practices [ 9 ].

Additionally, many medical professionals from non-English speaking countries aim to take international certification exams, such as the USMLE or PLAB, which are conducted in English, and ESP courses prepare students for these exams by familiarizing them with the medical terminology and language used in these assessments [ 10 ]. ESP courses also contribute to the professional development of medical students by improving their ability to write research papers, case reports, and other academic documents in English, which is essential for publishing in international journals and contributing to global medical knowledge [ 11 ]. In the increasingly interdisciplinary field of healthcare, collaboration with professionals from other countries is common, and ESP courses facilitate effective communication and collaboration with international colleagues, fostering innovation and the exchange of ideas [ 12 ].

With the rise of telemedicine and online medical consultations, proficiency in English is essential for non-English speaking medical professionals to provide remote healthcare services to international patients, and ESP courses prepare students for these modern medical practices [ 13 ].

Finally, ESP courses often include training on cultural competence, which is crucial for understanding and respecting the cultural backgrounds of patients and colleagues, leading to more empathetic and effective patient care and professional interactions [ 14 ]. Many ESP programs for medical students incorporate group presentations as a vital component of their curriculum, recognizing the positive impact on developing these essential skills [ 15 ].

Group projects in language courses, particularly in ESP for medical students, are highly relevant for several reasons. They provide a collaborative environment that mimics real-world professional settings, where healthcare professionals often work in multidisciplinary teams [ 16 ]. These group activities foster not only language skills but also crucial soft skills such as teamwork, leadership, and interpersonal communication, which are essential in medical practice [ 17 ].

The benefits of group projects over individual projects in language learning are significant. Hartono, Mujiyanto [ 18 ] found that group presentation tasks in ESP courses led to higher self-efficacy development compared to individual tasks. Group projects encourage peer learning, where students can learn from each other’s strengths and compensate for individual weaknesses [ 19 ]. They also provide a supportive environment that can reduce anxiety and increase willingness to communicate in the target language [ 20 ]. However, it is important to note that group projects also come with challenges, such as social loafing and unequal contribution, which need to be managed effectively [ 21 ].

Traditional lecture-based teaching methods, while valuable for knowledge acquisition, may not effectively prepare medical students for the interactive and collaborative nature of real-world healthcare settings [ 22 ]. Panel discussions (hereafter PDs), an interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, are particularly relevant in this context. They simulate professional conference discussions and interdisciplinary team meetings, preparing students for future academic and clinical environments where such skills are crucial [ 23 ].

PDs, also known as moderated discussions or moderated panels, are a specific type of interactive format where a group of experts or stakeholders engage in a facilitated conversation on a particular topic or issue [ 22 ]. In this format, a moderator guides the discussion, encourages active participation from all panelists, and fosters a collaborative environment that promotes constructive dialogue and critical thinking [ 24 ]. The goal is to encourage audience engagement and participation, which can be achieved through various strategies such as asking open-ended questions, encouraging counterpoints and counterarguments, and providing opportunities for audience members to pose questions or share their own experiences [ 25 ]. These discussions can take place in-person or online, and can be designed to accommodate diverse audiences and settings [ 26 ].

In this study, PD is considered a speaking activity where medical students are assigned specific roles to play during the simulation, such as a physician, quality improvement specialist, policymaker, or patient advocate. By taking on these roles, students can gain a better understanding of the diverse perspectives and considerations that come into play in real-world healthcare discussions [ 23 ]. Simulating PDs within ESP courses can be a powerful tool for enhancing medical students’ learning outcomes in multiple areas. This approach improves language proficiency, academic skills, and critical thinking abilities, while also enabling students to communicate effectively with diverse stakeholders in the medical field [ 27 , 28 ].

Theoretical framework

The panel discussions in our study are grounded in the concept of authentic assessment (outlined by Villarroel, Bloxham [ 29 ]), which involves designing tasks that mirror real-life situations and problems. In the context of medical education, this approach is particularly relevant as it prepares students for the complex, multidisciplinary nature of healthcare communication. Realism can be achieved through two means: providing a realistic context that describes and delivers a frame for the problem to be solved and creating tasks that are similar to those faced in real and/or professional life [ 30 ]. In our study, the PDs provide a realistic context by simulating scenarios where medical students are required to discuss and present complex medical topics in a professional setting, mirroring the types of interactions they will encounter in their future careers.

The task of participating in PDs also involves cognitive challenge, as students are required to think critically about complex medical topics, analyze information, and communicate their findings effectively. This type of task aims to generate processes of problem-solving, application of knowledge, and decision-making that correspond to the development of cognitive and metacognitive skills [ 23 ]. For medical students, these skills are crucial in developing clinical reasoning and effective patient communication. The PDs encourage students to go beyond the textual reproduction of fragmented and low-order content and move towards understanding, establishing relationships between new ideas and previous knowledge, linking theoretical concepts with everyday experience, deriving conclusions from the analysis of data, and examining both the logic of the arguments present in the theory and its practical scope [ 24 , 25 , 27 ].

Furthermore, the evaluative judgment aspect of our study is critical in helping students develop criteria and standards about what a good performance means in medical communication. This involves students judging their own performance and regulating their own learning [ 31 ]. In the context of panel discussions, students reflect on their own work, compare it with desired standards, and seek feedback from peers and instructors. By doing so, students can develop a sense of what constitutes good performance in medical communication and what areas need improvement [ 32 ]. Boud, Lawson and Thompson [ 33 ] argue that students need to build a precise judgment about the quality of their work and calibrate these judgments in the light of evidence. This skill is particularly important for future medical professionals who will need to continually assess and improve their communication skills throughout their careers.

The theoretical framework presented above highlights the importance of authentic learning experiences in medical education. By drawing on the benefits of group work and panel discussions, university instructor-researchers aimed to provide medical students with a unique opportunity to engage with complex cases and develop their communication and collaboration skills. As noted by Suryanarayana [ 34 ], authentic learning experiences can lead to deeper learning and improved retention. Considering the advantages of group work in promoting collaborative problem-solving and language development, the instructor-researchers designed a panel discussion task that simulates real-world scenarios, where students can work together to analyze complex cases, share knowledge, and present their findings to a simulated audience.

While previous studies have highlighted the benefits of interactive learning experiences and critical thinking skills in medical education, a research gap remains in understanding how medical students perceive the relevance of PDs in ESP courses. This study aims to address this gap by investigating medical students’ perceptions of PD tasks in ESP courses and how these perceptions relate to their language proficiency, critical thinking skills, and ability to communicate effectively with diverse stakeholders in the medical field. This understanding can inform best practices in medical education, contributing to the development of more effective communication skills for future healthcare professionals worldwide [ 23 ]. The research questions guiding this study are:

What are the perceived advantages of PDs from the perspectives of panelists and the audience?

What are the perceived disadvantages of PDs from the perspectives of panelists and the audience?

How can PDs be improved for panelists and the audience based on the insights of ESP instructors?

Methodology

Aim and design.

For this study, a two-phase qualitative design was employed to gain an understanding of the advantages and disadvantages of PDs from the perspectives of both student panelists and the audience (Phase 1) and to acquire an in-depth understanding of the suggested strategies provided by experts to enhance PPs for future students (Phase 2).

Participants and context of the study

This study was conducted in two phases (Fig.  1 ) at Shiraz University of Medical Sciences (SUMS), Shiraz, Iran.

Participants of the study in two phases

In the first phase, the student participants were 46 non-native speakers of English and international students who studied medicine at SUMS. Their demographic characteristics can be seen in Table  1 .

These students were purposefully selected because they were the only SUMS international students who had taken the ESP (English for Specific Purposes) course. The number of international students attending SUMS is indeed limited. Each year, a different batch of international students joins the university. They progress through a sequence of English courses, starting with General English 1 and 2, followed by the ESP course, and concluding with academic writing. At the time of data collection, the students included in the study were the only international students enrolled in the ESP course. This mandatory 3-unit course is designed to enhance their language and communication skills specifically tailored to their profession. As a part of the Medicine major curriculum, this course aims to improve their English language proficiency in areas relevant to medicine, such as understanding medical terminology, comprehending original medicine texts, discussing clinical cases, and communicating with patients, colleagues, and other healthcare professionals.

Throughout the course, students engage in various interactive activities, such as group discussions, role-playing exercises, and case studies, to develop their practical communication skills. In this course, medical students receive four marks out of 20 for their oral presentations, while the remaining marks are allocated to their written midterm and final exams. From the beginning of the course, they are briefed about PDs, and they are shown two YouTube-downloaded videos about PDs at medical conferences, a popular format for discussing and sharing knowledge, research findings, and expert opinions on various medical topics.

For the second phase of the study, a specific group of participants was purposefully selected. This group consisted of three faculty members from SUMS English department who had extensive experience attending numerous conferences at national and international levels, particularly in the medical field, as well as working as translators and interpreters in medical congresses. Over the course of ten years, they also gained considerable experience in PDs. They were invited to discuss strategies helpful for medical students with PDs.

Panel discussion activity design and implementation

When preparing for a PD session, medical students received comprehensive guidance on understanding the roles and responsibilities of each panel member. This guidance was aimed at ensuring that each participant was well-prepared and understood their specific role in the discussion.

Moderators should play a crucial role in steering the conversation. They are responsible for ensuring that all panelists have an opportunity to contribute and that the audience is engaged effectively. Specific tasks include preparing opening remarks, introducing panelists, and crafting transition questions to facilitate smooth topic transitions. The moderators should also manage the time to ensure balanced participation and encourage active audience involvement.

Panelists are expected to be subject matter experts who bring valuable insights and opinions to the discussion. They are advised to conduct thorough research on the topic and prepare concise talking points. Panelists are encouraged to draw from their medical knowledge and relevant experiences, share evidence-based information, and engage with other panelists’ points through active listening and thoughtful responses.

The audience plays an active role in the PDs. They are encouraged to participate by asking questions, sharing relevant experiences, and contributing to the dialogue. To facilitate this, students are advised to take notes during the discussion and think of questions or comments they can contribute during the Q&A segment.

For this special course, medical students were advised to choose topics either from their ESP textbook or consider current medical trends, emerging research, and pressing issues in their field. Examples included breast cancer, COVID-19, and controversies in gene therapy. The selection process involved brainstorming sessions and consultation with the course instructor to ensure relevance and appropriateness.

To accommodate the PD sessions within the course structure, students were allowed to start their PD sessions voluntarily from the second week. However, to maintain a balance between peer-led discussions and regular course content, only one PD was held weekly. This approach enabled the ESP lecturer to deliver comprehensive content while also allowing students to engage in these interactive sessions.

A basic time structure was suggested for each PD (Fig.  2 ):

Time allocation for panel discussion stages in minutes

To ensure the smooth running of the course and maintain momentum, students were informed that they could cancel their PD session only once. In such cases, they were required to notify the lecturer and other students via the class Telegram channel to facilitate rescheduling and minimize disruptions. This provision was essential in promoting a sense of community among students and maintaining the course’s continuity.

Research tools and data collection

The study utilized various tools to gather and analyze data from participants and experts, ensuring a comprehensive understanding of the research topic.

Reflection papers

In Phase 1 of the study, 46 medical students detailed their perceptions of the advantages and disadvantages of panel discussions from dual perspectives: as panelists (presenters) and as audience members (peers).

Participants were given clear instructions and a 45-minute time frame to complete the reflection task. With approximately 80% of the international language students being native English speakers and the rest fluent in English, the researchers deemed this time allocation reasonable. The questions and instructions were straightforward, facilitating quick comprehension. It was estimated that native English speakers would need about 30 min to complete the task, while non-native speakers might require an extra 15 min for clarity and expression. This time frame aimed to allow students to respond thoughtfully without feeling rushed. Additionally, students could request more time if needed.

Focus group discussion

In phase 2 of the study, a focus group discussion was conducted with three expert participants. The purpose of the focus group was to gather insights from expert participants, specifically ESP (English for Specific Purposes) instructors, on how presentation dynamics can be improved for both panelists and the audience.

According to Colton and Covert [ 35 ], focus groups are useful for obtaining detailed input from experts. The appropriate size of a focus group is determined by the study’s scope and available resources [ 36 ]. Morgan [ 37 ] suggests that small focus groups are suitable for complex topics where specialist participants might feel frustrated if not allowed to express themselves fully.

The choice of a focus group over individual interviews was based on several factors. First, the exploratory nature of the study made focus groups ideal for interactive discussions, generating new ideas and in-depth insights [ 36 ]. Second, while focus groups usually involve larger groups, they can effectively accommodate a limited number of experts with extensive knowledge [ 37 ]. Third, the focus group format fostered a more open environment for idea exchange, allowing participants to engage dynamically [ 36 ]. Lastly, conducting a focus group was more time- and resource-efficient than scheduling three separate interviews [ 36 ].

Data analysis

The first phase of the study involved a thorough examination of the data related to the research inquiries using thematic analysis. This method was chosen for its effectiveness in uncovering latent patterns from a bottom-up perspective, facilitating a comprehensive understanding of complex educational phenomena [ 38 ]. The researchers first familiarized themselves with the data by repeatedly reviewing the reflection papers written by the medical students. Next, an initial round of coding was independently conducted to identify significant data segments and generate preliminary codes that reflected the students’ perceptions of the advantages and disadvantages of presentation dynamics PDs from both the presenter and audience viewpoints [ 38 ].

The analysis of the reflection papers began with the two researchers coding a subset of five papers independently, adhering to a structured qualitative coding protocol [ 39 ]. They convened afterward to compare their initial codes and address any discrepancies. Through discussion, they reached an agreement on the codes, which were then analyzed, organized into categories and themes, and the frequency of each code was recorded [ 38 ].

After coding the initial five papers, the researchers continued to code the remaining 41 reflection paper transcripts in batches of ten, meeting after each batch to review their coding, resolve any inconsistencies, and refine the coding framework as needed. This iterative process, characterized by independent coding, joint reviews, and consensus-building, helped the researchers establish a robust and reliable coding approach consistently applied to the complete dataset [ 40 ]. Once all 46 reflection paper transcripts were coded, the researchers conducted a final review and discussion to ensure accurate analysis. They extracted relevant excerpts corresponding to the identified themes and sub-themes from the transcripts to provide detailed explanations and support for their findings [ 38 ]. This multi-step approach of separate initial coding, collaborative review, and frequency analysis enhanced the credibility and transparency of the qualitative data analysis.

To ensure the trustworthiness of the data collected in this study, the researchers adhered to the Guba and Lincoln standards of scientific accuracy in qualitative research, which encompass credibility, confirmability, dependability, and transferability [ 41 ] (Table  2 ).

The analysis of the focus group data obtained from experts followed the same rigorous procedure applied to the student participants’ data. Thematic analysis was employed to examine the experts’ perspectives, maintaining consistency in the analytical approach across both phases of the study. The researchers familiarized themselves with the focus group transcript, conducted independent preliminary coding, and then collaboratively refined the codes. These codes were subsequently organized into categories and themes, with the frequency of each code recorded. The researchers engaged in thorough discussions to ensure agreement on the final themes and sub-themes. Relevant excerpts from the focus group transcript were extracted to provide rich, detailed explanations of each theme, thereby ensuring a comprehensive and accurate analysis of the experts’ insights.

1. What are the advantages of PDs from the perspective of panelists and the audience?

The analysis of the advantages of PDs from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  2 and 3 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  3 ), the overarching theme was “Personal and Professional Development.” The most frequently reported advantage was knowledge sharing (93.5%), followed closely by increased confidence (91.3%) and the importance of interaction in presentations (91.3%).

Notably, all categories within this theme had at least one code mentioned by over 80% of participants, indicating a broad range of perceived benefits. The category of “Effective teamwork and communication” was particularly prominent, with collaboration (89.1%) and knowledge sharing (93.5%) being among the most frequently cited advantages. This suggests that PDs are perceived as valuable tools for fostering interpersonal skills and collective learning. In the “Language mastery” category, increased confidence (91.3%) and better retention of key concepts (87.0%) were highlighted, indicating that PDs are seen as effective for both language and content learning.

The audience perspective (Table  4 ), encapsulated under the theme “Enriching Learning Experience,” showed similarly high frequencies across all categories.

The most frequently mentioned advantage was exposure to diverse speakers (93.5%), closely followed by the range of topics covered (91.3%) and increased audience interest (91.3%). The “Broadening perspectives” category was particularly rich, with all codes mentioned by over 70% of participants. This suggests that audience members perceive PDs as valuable opportunities for expanding their knowledge and viewpoints. In the “Language practice” category, the opportunity to practice language skills (89.1%) was the most frequently cited advantage, indicating that even as audience members, students perceive significant language learning benefits.

Comparing the two perspectives reveals several interesting patterns:

High overall engagement: Both panelists and audience members reported high frequencies across all categories, suggesting that PDs are perceived as beneficial regardless of the role played.

Language benefits: While panelists emphasized increased confidence (91.3%) and better retention of concepts (87.0%), audience members highlighted opportunities for language practice (89.1%). This indicates that PDs offer complementary language learning benefits for both roles.

Interactive learning: The importance of interaction was highly rated by panelists (91.3%), while increased audience interest was similarly valued by the audience (91.3%). This suggests that PDs are perceived as an engaging, interactive learning method from both perspectives.

Professional development: Panelists uniquely emphasized professional growth aspects such as experiential learning (84.8%) and real-world application (80.4%). These were not directly mirrored in the audience perspective, suggesting that active participation in PDs may offer additional professional development benefits.

Broadening horizons: Both groups highly valued the diversity aspect of PDs. Panelists appreciated diversity and open-mindedness (80.4%), while audience members valued diverse speakers (93.5%) and a range of topics (91.3%).

2. What are the disadvantages of PDs from the perspective of panelists and the audience?

The analysis of the disadvantages of panel discussions (PDs) from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  4 and 5 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  5 ), the theme “Drawbacks of PDs” was divided into two main categories: “Academic Workload Challenges” and “Coordination Challenges.” The most frequently reported disadvantage was long preparation (87.0%), followed by significant practice needed (82.6%) and the time-consuming nature of PDs (80.4%). These findings suggest that the primary concern for panelists is the additional workload that PDs impose on their already demanding academic schedules. The “Coordination Challenges” category, while less prominent than workload issues, still presented significant concerns. Diverse panel skills (78.3%) and finding suitable panelists (73.9%) were the most frequently cited issues in this category, indicating that team dynamics and composition are notable challenges for panelists.

The audience perspective (Table  6 ), encapsulated under the theme “Drawbacks of PDs,” was divided into two main categories: “Time-related Issues” and “Interaction and Engagement Issues.” In the “Time-related Issues” category, the most frequently mentioned disadvantage was the inefficient use of time (65.2%), followed by the perception of PDs as too long and boring (60.9%). Notably, 56.5% of respondents found PDs stressful due to overwhelming workload from other studies, and 52.2% considered them not very useful during exam time. The “Interaction and Engagement Issues” category revealed more diverse concerns. The most frequently mentioned disadvantage was the repetitive format (82.6%), followed by limited engagement with the audience (78.3%) and the perception of PDs as boring (73.9%). The audience also noted issues related to the panelists’ preparation and coordination, such as “Not practiced and natural” (67.4%) and “Coordination and Interaction Issues” (71.7%), suggesting that the challenges faced by panelists directly impact the audience’s experience.

Workload concerns: Both panelists and audience members highlighted time-related issues. For panelists, this manifested as long preparation times (87.0%) and difficulty balancing with other studies (76.1%). For the audience, it appeared as perceptions of inefficient use of time (65.2%) and stress due to overwhelming workload from other studies (56.5%).

Engagement issues: While panelists focused on preparation and coordination challenges, the audience emphasized the quality of the discussion and engagement. This suggests a potential mismatch between the efforts of panelists and the expectations of the audience.

Boredom and repetition: The audience frequently mentioned boredom (73.9%) and repetitive format (82.6%) as issues, which weren’t directly mirrored in the panelists’ responses. This indicates that while panelists may be focused on content preparation, the audience is more concerned with the delivery and variety of the presentation format.

Coordination challenges: Both groups noted coordination issues, but from different perspectives. Panelists struggled with team dynamics and finding suitable co-presenters, while the audience observed these challenges manifesting as unnatural or unpracticed presentations.

Academic pressure: Both groups acknowledged the strain PDs put on their academic lives, with panelists viewing it as a burden (65.2%) and the audience finding it less useful during exam times (52.2%).

3. How can PDs be improved for panelists and the audience from the experts’ point of view?

The presentation of data for this research question differs from the previous two due to the unique nature of the information gathered. Unlike the quantifiable student responses in earlier questions, this data stems from expert opinions and a reflection discussion session, focusing on qualitative recommendations for improvement rather than frequency of responses (Braun & Clarke, 2006). The complexity and interconnectedness of expert suggestions, coupled with the integration of supporting literature, necessitate a more narrative approach (Creswell & Poth, 2018). This format allows for a richer exploration of the context behind each recommendation and its potential implications (Patton, 2015). Furthermore, the exploratory nature of this question, aimed at generating ideas for improvement rather than measuring prevalence of opinions, is better served by a detailed, descriptive presentation (Merriam & Tisdell, 2016). This approach enables a more nuanced understanding of how PDs can be enhanced, aligning closely with the “how” nature of the research question and providing valuable insights for potential implementation (Yin, 2018).

The experts provided several suggestions to address the challenges faced by students in panel discussions (PDs) and improve the experience for both panelists and the audience. Their recommendations focused on six key areas: time management and workload, preparation and skill development, engagement and interactivity, technological integration, collaboration and communication, and institutional support.

To address the issue of time management and heavy workload, one expert suggested teaching students to “ break down the task to tackle the time-consuming nature of panel discussions and balance it with other studies .” This approach aims to help students manage the extensive preparation time required for PDs without compromising their other academic responsibilities. Another expert emphasized “ enhancing medical students’ abilities to prioritize tasks , allocate resources efficiently , and optimize their workflow to achieve their goals effectively .” These skills were seen as crucial not only for PD preparation but also for overall academic success and future professional practice.

Recognizing the challenges of long preparation times and the perception of PDs being burdensome, an expert proposed “ the implementation of interactive training sessions for panelists .” These sessions were suggested to enhance coordination skills and improve the ability of group presenters to engage with the audience effectively. The expert emphasized that such training could help students view PDs as valuable learning experiences rather than additional burdens, potentially increasing their motivation and engagement in the process.

To combat issues of limited engagement and perceived boredom, experts recommended increasing engagement opportunities for the audience through interactive elements like audience participation and group discussions. They suggested that this could transform PDs from passive listening experiences to active learning opportunities. One expert suggested “ optimizing time management and restructuring the format of panel discussions ” to address inefficiency during sessions. This restructuring could involve shorter presentation segments interspersed with interactive elements to maintain audience attention and engagement.

An innovative solution proposed by one expert was “ using ChatGPT to prepare for PDs by streamlining scenario presentation preparation and role allocation. ” The experts collectively discussed the potential of AI to assist medical students in reducing their workload and saving time in preparing scenario presentations and allocating roles in panel discussions. They noted that AI could help generate initial content drafts, suggest role distributions based on individual strengths, and even provide practice questions for panelists, significantly reducing preparation time while maintaining quality.

Two experts emphasized the importance of enhancing collaboration and communication among panelists to address issues related to diverse panel skills and coordination challenges. They suggested establishing clear communication channels and guidelines to improve coordination and ensure a cohesive presentation. This could involve creating structured team roles, setting clear expectations for each panelist, and implementing regular check-ins during the preparation process to ensure all team members are aligned and progressing.

All experts were in agreement that improving PDs would not be possible “ if nothing is done by the university administration to reduce the ESP class size for international students .” They believed that large class sizes in ESP or EFL classes could negatively influence group oral presentations, hindering language development and leading to uneven participation. The experts suggested that smaller class sizes would allow for more individualized attention, increased speaking opportunities for each student, and more effective feedback mechanisms, all of which are crucial for developing strong presentation skills in a second language.

Research question 1: what are the advantages of PDs from the perspective of panelists and the audience?

The results of this study reveal significant advantages of PDs for both panelists and audience members in the context of medical education. These findings align with and expand upon previous research in the field of educational presentations and language learning.

Personal and professional development for panelists

The high frequency of reported benefits in the “Personal and Professional Development” theme for panelists aligns with several previous studies. The emphasis on language mastery, particularly increased confidence (91.3%) and better retention of key concepts (87.0%), supports the findings of Hartono, Mujiyanto [ 42 ], Gedamu and Gezahegn [ 15 ], Li [ 43 ], who all highlighted the importance of language practice in English oral presentations. However, our results show a more comprehensive range of benefits, including professional growth aspects like experiential learning (84.8%) and real-world application (80.4%), which were not as prominently featured in these earlier studies.

Interestingly, our findings partially contrast with Chou [ 44 ] study, which found that while group oral presentations had the greatest influence on improving students’ speaking ability, individual presentations led to more frequent use of metacognitive, retrieval, and rehearsal strategies. Our results suggest that PDs, despite being group activities, still provide significant benefits in these areas, possibly due to the collaborative nature of preparation and the individual responsibility each panelist bears. The high frequency of knowledge sharing (93.5%) and collaboration (89.1%) in our study supports Harris, Jones and Huffman [ 45 ] emphasis on the importance of group dynamics and varied perspectives in educational settings. However, our study provides more quantitative evidence for these benefits in the specific context of PDs.

Enriching learning experience for the audience

The audience perspective in our study reveals a rich learning experience, with high frequencies across all categories. This aligns with Agustina [ 46 ] findings in business English classes, where presentations led to improvements in all four language skills. However, our study extends these findings by demonstrating that even passive participation as an audience member can lead to significant perceived benefits in language practice (89.1%) and broadening perspectives (93.5% for diverse speakers). The high value placed on diverse speakers (93.5%) and range of topics (91.3%) by the audience supports the notion of PDs as a tool for expanding knowledge and viewpoints. This aligns with the concept of situated learning experiences leading to deeper understanding in EFL classes, as suggested by Li [ 43 ] and others [ 18 , 31 ]. However, our study provides more specific evidence for how this occurs in the context of PDs.

Interactive learning and engagement

Both panelists and audience members in our study highly valued the interactive aspects of PDs, with the importance of interaction rated at 91.3% by panelists and increased audience interest at 91.3% by the audience. This strong emphasis on interactivity aligns with Azizi and Farid Khafaga [ 19 ] study on the benefits of dynamic assessment and dialogic learning contexts. However, our study provides more detailed insights into how this interactivity is perceived and valued by both presenters and audience members in PDs.

Professional growth and real-world application

The emphasis on professional growth through PDs, particularly for panelists, supports Li’s [ 43 ] assertion about the power of oral presentations as situated learning experiences. Our findings provide more specific evidence for how PDs contribute to professional development, with high frequencies reported for experiential learning (84.8%) and real-world application (80.4%). This suggests that PDs may be particularly effective in bridging the gap between academic learning and professional practice in medical education.

Research question 2: what are the disadvantages of pds from the perspective of panelists and the audience?

Academic workload challenges for panelists.

The high frequency of reported challenges in the “Academic Workload Challenges” category for panelists aligns with several previous studies in medical education [ 47 , 48 , 49 ]. The emphasis on long preparation (87.0%), significant practice needed (82.6%), and the time-consuming nature of PDs (80.4%) supports the findings of Johnson et al. [ 24 ], who noted that while learners appreciate debate-style journal clubs in health professional education, they require additional time commitment. This is further corroborated by Nowak, Speed and Vuk [ 50 ], who found that intensive learning activities in medical education, while beneficial, can be time-consuming for students.

Perceived value of pds relative to time investment

While a significant portion of the audience (65.2%) perceived PDs as an inefficient use of time, the high frequency of engagement-related concerns (82.6% for repetitive format, 78.3% for limited engagement) suggests that the perceived lack of value may be more closely tied to the quality of the experience rather than just the time investment. This aligns with Dyhrberg O’Neill [ 27 ] findings on debate-based oral exams, where students perceived value despite the time-intensive nature of the activity. However, our results indicate a more pronounced concern about the return on time investment in PDs. This discrepancy might be addressed through innovative approaches to PD design and implementation, such as those proposed by Almazyad et al. [ 22 ], who suggested using AI tools to enhance expert panel discussions and potentially improve efficiency.

Coordination challenges for panelists

The challenges related to coordination in medical education, such as diverse panel skills (78.3%) and finding suitable panelists (73.9%), align with previous research on teamwork in higher education [ 21 ]. Our findings support the concept of the free-rider effect discussed by Hall and Buzwell [ 21 ], who explored reasons for non-contribution in group projects beyond social loafing. This is further elaborated by Mehmood, Memon and Ali [ 51 ], who proposed that individuals may not contribute their fair share due to various factors including poor communication skills or language barriers, which is particularly relevant in medical education where clear communication is crucial [ 52 ]. Comparing our results to other collaborative learning contexts in medical education, Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] measured teamwork competence development in a multidisciplinary project-based learning environment. They found that while teamwork skills improved over time, initial coordination challenges were significant. This aligns with our findings on the difficulties of coordinating diverse panel skills and opinions in medical education settings.

Our results also resonate with Chou’s [ 44 ] study comparing group and individual oral presentations, which found that group presenters often had a limited understanding of the overall content. This is supported by Wilson, Ho and Brookes [ 54 ], who examined student perceptions of teamwork in undergraduate science degrees, highlighting the challenges and benefits of collaborative work, which are equally applicable in medical education [ 52 ].

Quality of discussions and perception for the audience

The audience perspective in our study reveals significant concerns about the quality and engagement of PDs in medical education. The high frequency of issues such as repetitive format (82.6%) and limited engagement with the audience (78.3%) aligns with Parmar and Bickmore [ 55 ] findings on the importance of addressing individual audience members and gathering feedback. This is further supported by Nurakhir et al. [ 25 ], who explored students’ views on classroom debates as a strategy to enhance critical thinking and oral communication skills in nursing education, which shares similarities with medical education. Comparing our results to other interactive learning methods in medical education, Jones et al. [ 26 ] reviewed the use of journal clubs and book clubs in pharmacy education. They found that while these methods enhanced engagement, they also faced challenges in maintaining student interest over time, similar to the boredom issues reported in our study of PDs in medical education. The perception of PDs as boring (73.9%) and not very useful during exam time (52.2%) supports previous research on the stress and pressure experienced by medical students [ 48 , 49 ]. Grieve et al. [ 20 ] specifically examined student fears of oral presentations and public speaking in higher education, which provides context for the anxiety and disengagement observed in our study of medical education. Interestingly, Bhuvaneshwari et al. [ 23 ] found positive impacts of panel discussions in educating medical students on specific modules. This contrasts with our findings and suggests that the effectiveness of PDs in medical education may vary depending on the specific context and implementation.

Comparative analysis and future directions

Our study provides a unique comparative analysis of the challenges faced by both panelists and audience members in medical education. The alignment of concerns around workload and time management between the two groups suggests that these are overarching issues in the implementation of PDs in medical curricula. This is consistent with the findings of Pasandín et al. [ 56 ], who examined cooperative oral presentations in higher education and their impact on both technical and soft skills, which are crucial in medical education [ 52 ]. The mismatch between panelist efforts and audience expectations revealed in our study is a novel finding that warrants further investigation in medical education. This disparity could be related to the self-efficacy beliefs of presenters, as explored by Gedamu and Gezahegn [ 15 ] in their study of TEFL trainees’ attitudes towards academic oral presentations, which may have parallels in medical education. Looking forward, innovative approaches could address some of the challenges identified in medical education. Almazyad et al. [ 22 ] proposed using AI tools like ChatGPT to enhance expert panel discussions in pediatric palliative care, which could potentially address some of the preparation and engagement issues identified in our study of medical education. Additionally, Ragupathi and Lee [ 57 ] discussed the role of rubrics in higher education, which could provide clearer expectations and feedback for both panelists and audience members in PDs within medical education.

Research question 3: how can PDs be improved for panelists and the audience from the experts’ point of view?

The expert suggestions for improving PDs address several key challenges identified in previous research on academic presentations and student workload management. These recommendations align with current trends in educational technology and pedagogical approaches, while also considering the unique needs of medical students.

The emphasis on time management and workload reduction strategies echoes findings from previous studies on medical student stress and academic performance. Nowak, Speed and Vuk [ 50 ] found that medical students often struggle with the fast-paced nature of their courses, which can lead to reduced motivation and superficial learning approaches. The experts’ suggestions for task breakdown and prioritization align with Rabbi and Islam [ 58 ] recommendations for reducing workload stress through effective assignment prioritization. Additionally, Popa et al. [ 59 ] highlight the importance of acceptance and planning in stress management for medical students, supporting the experts’ focus on these areas.

The proposed implementation of interactive training sessions for panelists addresses the need for enhanced presentation skills in professional contexts, a concern highlighted by several researchers [ 17 , 60 ]. This aligns with Grieve et al. [ 20 ] findings on student fears of oral presentations and public speaking in higher education, emphasizing the need for targeted training. The focus on interactive elements and audience engagement also reflects current trends in active learning pedagogies, as demonstrated by Pasandín et al. [ 56 ] in their study on cooperative oral presentations in engineering education.

The innovative suggestion to use AI tools like ChatGPT for PD preparation represents a novel approach to leveraging technology in education. This aligns with recent research on the potential of AI in scientific research, such as the study by Almazyad et al. [ 22 ], which highlighted the benefits of AI in supporting various educational tasks. However, it is important to consider potential ethical implications and ensure that AI use complements rather than replaces critical thinking and creativity.

The experts’ emphasis on enhancing collaboration and communication among panelists addresses issues identified in previous research on teamwork in higher education. Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] noted the importance of measuring teamwork competence development in project-based learning environments. The suggested strategies for improving coordination align with best practices in collaborative learning, as demonstrated by Romero-Yesa et al. [ 61 ] in their qualitative assessment of challenge-based learning and teamwork in electronics programs.

The unanimous agreement on the need to reduce ESP class sizes for international students reflects ongoing concerns about the impact of large classes on language learning and student engagement. This aligns with research by Li [ 3 ] on issues in developing EFL learners’ oral English communication skills. Bosco et al. [ 62 ] further highlight the challenges of teaching and learning ESP in mixed classes, supporting the experts’ recommendation for smaller class sizes. Qiao, Xu and bin Ahmad [ 63 ] also emphasize the implementation challenges for ESP formative assessment in large classes, further justifying the need for reduced class sizes.

These expert recommendations provide a comprehensive approach to improving PDs, addressing not only the immediate challenges of preparation and delivery but also broader issues of student engagement, workload management, and institutional support. By implementing these suggestions, universities could potentially transform PDs from perceived burdens into valuable learning experiences that enhance both academic and professional skills. This aligns with Kho and Ting [ 64 ] systematic review on overcoming oral presentation anxiety among tertiary ESL/EFL students, which emphasizes the importance of addressing both challenges and strategies in improving presentation skills.

This study has shed light on the complex challenges associated with PDs in medical education, revealing a nuanced interplay between the experiences of panelists and audience members. The findings underscore the need for a holistic approach to implementing PDs that addresses both the academic workload concerns and the quality of engagement.

Our findings both support and extend previous research on the challenges of oral presentations and group work in medical education settings. The high frequencies of perceived challenges across multiple categories for both panelists and audience members suggest that while PDs may offer benefits, they also present significant obstacles that need to be addressed in medical education. These results highlight the need for careful consideration in the implementation of PDs in medical education, with particular attention to workload management, coordination strategies, and audience engagement techniques. Future research could focus on developing and testing interventions to mitigate these challenges while preserving the potential benefits of PDs in medical education.

Moving forward, medical educators should consider innovative approaches to mitigate these challenges. This may include:

Integrating time management and stress coping strategies into the PD preparation process [ 59 ].

Exploring the use of AI tools to streamline preparation and enhance engagement [ 22 ].

Developing clear rubrics and expectations for both panelists and audience members [ 57 ].

Incorporating interactive elements to maintain audience interest and participation [ 25 ].

Limitations and future research

One limitation of this study is that it focused on a specific population of medical students, which may limit the generalizability of the findings to other student populations. Additionally, the study relied on self-report data from panelists and audience members, which may introduce bias and affect the validity of the results. Future research could explore the effectiveness of PDs in different educational contexts and student populations to provide a more comprehensive understanding of the benefits and challenges of panel discussions.

Future research should focus on evaluating the effectiveness of these interventions and exploring how PDs can be tailored to the unique demands of medical education. By addressing the identified challenges, PDs have the potential to become a more valuable and engaging component of medical curricula, fostering both academic and professional development. Ultimately, the goal should be to transform PDs from perceived burdens into opportunities for meaningful learning and skill development, aligning with the evolving needs of medical education in the 21st century.

Future research could also examine the long-term impact of PDs on panelists’ language skills, teamwork, and communication abilities. Additionally, exploring the effectiveness of different training methods and tools, such as AI technology, in improving coordination skills and reducing workload stress for panelists could provide valuable insights for educators and administrators. Further research could also investigate the role of class size and audience engagement in enhancing the overall effectiveness of PDs in higher education settings. By addressing these gaps in the literature, future research can contribute to the ongoing development and improvement of PDs as a valuable learning tool for students in higher education.

However, it is important to note that implementing these changes may require significant institutional resources and a shift in pedagogical approaches. Future research could focus on piloting these recommendations and evaluating their effectiveness in improving student outcomes and experiences with PDs.

Data availability

We confirm that the data supporting the findings are available within this article. Raw data supporting this study’s findings are available from the corresponding author, upon request.

Abbreviations

Artificial Intelligence

English as a Foreign Language

English for Specific Purposes

Panel Discussion

Shiraz University of Medical Sciences

Harden RM, Laidlaw JM. Essential skills for a medical teacher: an introduction to teaching and learning in medicine. Elsevier Health Sciences; 2020.

Ibrahim Mohamed O, Al Jadaan DO. English for Specific purposes (Esp) Needs Analysis for Health Sciences students: a cross-sectional study at a University in the UAE. English for Specific purposes (Esp) Needs Analysis for Health Sciences Students: A Cross-Sectional Study at a University in the UAE.

Li Y, Heron M. English for general academic purposes or English for specific purposes? Language learning needs of medical students at a Chinese university. Theory Pract Lang Stud. 2021;11(6):621–31.

Article   Google Scholar  

Chan SMH, Mamat NH, Nadarajah VD. Mind your language: the importance of English language skills in an International Medical Programme (IMP). BMC Med Educ. 2022;22(1):405.

Cortez Faustino BS, Ticas de Córdova CK, de la Hernández DI. Teaching English for specific purposes: contents and methodologies that could be implemented in the English for Medical purposes (EMP) course for the doctor of Medicine Major at the University of El Salvador. Universidad de El Salvador; 2022.

BENYAMINA E-Z BOUKAHLAH. Enhancing Specialty Language learning through content-based instruction: students of Paramedical Institute of Tiaret as a case study. Université IBN KHALDOUN-Tiaret; 2023.

Prikazchikov M. Medical English course for russian-speaking dentists: a needs analysis study. Iowa State University; 2024.

Kim C, Lee SY, Park S-H. Is Korea Ready to be a key player in the Medical Tourism Industry? An English Education Perspective. Iran J Public Health. 2020;49(2):267–73.

Google Scholar  

Syakur A, Zainuddin H, Hasan MA. Needs analysis English for specific purposes (esp) for vocational pharmacy students. Budapest International Research and Critics in Linguistics and Education (BirLE). Journal. 2020;3(2):724–33.

Chan S, Taylor L. Comparing writing proficiency assessments used in professional medical registration: a methodology to inform policy and practice. Assess Writ. 2020;46:100493.

Hyland K, Jiang FK. Delivering relevance: the emergence of ESP as a discipline. Engl Specif Purp. 2021;64:13–25.

Maftuna B. The role of English in ESP. Am J Adv Sci Res. 2024;1(2):1–5.

LEON LI, HUMANIZING THE FOREIGN LANGUAGE. COURSE: NEW TEACHING METHODS FOR MEDICAL STUDENTS. Language, Culture and Change. 2022:243.

Dahm MR, Yates L. Rapport, empathy and professional identity: Some challenges for international medical graduates speaking English as a second or foreign language. Multilingual Healthcare: A Global View on Communicative Challenges. 2020:209 – 34.

Gedamu AD, Gezahegn TH. TEFL trainees’ attitude to and self-efficacy beliefs of academic oral presentation. Cogent Educ. 2023;10(1):2163087.

Saliu B, Hajrullai H. Best practices in the English for specific purpose classes at the language center. Procedia-Social Behav Sci. 2016;232:745–9.

Clokie TL, Fourie E. Graduate employability and communication competence: are undergraduates taught relevant skills? Bus Prof Communication Q. 2016;79(4):442–63.

Hartono H, Mujiyanto J, Fitriati SW, Sakhiyya Z, Lotfie MM, Maharani MM. English Presentation Self-Efficacy Development of Indonesian ESP students: the effects of Individual versus Group Presentation tasks. Int J Lang Educ. 2023;7(3):361–76.

Azizi Z, Farid Khafaga A. Scaffolding via Group-dynamic Assessment to positively affect motivation, learning anxiety, and willingness to Communicate: a Case Study of High School Students. J Psycholinguist Res. 2023;52(3):831–51.

Grieve R, Woodley J, Hunt SE, McKay A. Student fears of oral presentations and public speaking in higher education: a qualitative survey. J Furth High Educ. 2021;45(9):1281–93.

Hall D, Buzwell S. The problem of free-riding in group projects: looking beyond social loafing as reason for non-contribution. Act Learn High Educ. 2013;14(1):37–49.

Almazyad M, Aljofan F, Abouammoh NA, Muaygil R, Malki KH, Aljamaan F, et al. Enhancing Expert Panel discussions in Pediatric Palliative Care: innovative scenario development and summarization with ChatGPT-4. Cureus. 2023;15(4):e38249.

Bhuvaneshwari S, Rashmi R, Deepika K, Anirudh VM, Vijayamathy A, Rekha S, Kathiravan R. Impact of panel discussion in educating AETCOM First Module among Undergraduate Medical Students. Latin Am J Pharmacy: Life Sci J. 2023;42(6):407–12.

Johnson BR, Logan LD, Darley A, Stone RH, Smith SE, Osae SP, et al. A scoping review for Debate-Style Journal Clubs in Health Professional Education. Am J Pharm Educ. 2023;87(6):100064.

Nurakhir A, Palupi FN, Langeveld C, Nurmalia D. Students’ views of classroom debates as a strategy to enhance critical thinking and oral communication skills. 2020.

Jones EP, Nelson NR, Thorpe CT, Rodgers PT, Carlson RB. Use of journal clubs and book clubs in pharmacy education: a scoping review. Currents Pharm Teach Learn. 2022;14(1):110–9.

Dyhrberg O’Neill L. Assessment of student debates in support of active learning? Students’ perceptions of a debate-based oral final exam. Act Learn High Educ. 2024.

Dyment JE, O’Connell TS. Assessing the quality of reflection in student journals: a review of the research. Teach High Educ. 2011;16(1):81–97.

Villarroel V, Bloxham S, Bruna D, Bruna C, Herrera-Seda C. Authentic assessment: creating a blueprint for course design. Assess Evaluation High Educ. 2018;43(5):840–54.

Schultz M, Young K, Gunning K, Harvey T. Defining and measuring authentic assessment: a case study in the context of tertiary science. Assess Evaluation High Educ. 2022;47(1):77–94.

Sundrarajun C, Kiely R. The oral presentation as a context for learning and assessment. Innov Lang Learn Teach. 2010;4(2):101–17.

Wyatt-Smith C, Adie L. The development of students’ evaluative expertise: enabling conditions for integrating criteria into pedagogic practice. J Curriculum Stud. 2021;53(4):399–419.

Boud D, Lawson R, Thompson DG. The calibration of student judgement through self-assessment: disruptive effects of assessment patterns. High Educ Res Dev. 2015;34(1):45–59.

A. S. Enhancing Meaningful Learning experiences through Comprehension and Retention by students. Twentyfirst Century Publications Patiala. 2023;49.

Colton D, Covert RW. Designing and constructing instruments for social research and evaluation. Wiley; 2007.

Krueger RA, Casey MA. Focus group interviewing. Handbook of practical program evaluation. 2015:506 – 34.

Morgan DL. Handbook of interview research: Context and method. Oaks, CA, USA: Sage Publications Thousand; 2002.

Braun V, Clarke V. Conceptual and design thinking for thematic analysis. Qualitative Psychol. 2022;9(1):3.

Elliott V. Thinking about the coding process in qualitative data analysis. Qualitative Rep. 2018;23(11).

Syed M, Nelson SC. Guidelines for establishing reliability when coding narrative data. Emerg Adulthood. 2015;3(6):375–87.

Lincoln Y. Naturalistic inquiry: Sage; 1985.

Hartono H, Mujiyanto J, Fitriati SW, Sakhiyya Z, Lotfie MM, Maharani MM. English presentation self-efficacy development of Indonesian ESP students: the effects of Individual versus Group Presentation tasks. Int J Lang Educ. 2023;7(3).

Li X. Teaching English oral presentations as a situated task in an EFL classroom: a quasi-experimental study of the effect of video-assisted self-reflection. Revista Signos. 2018;51(98):359–81.

Chou M-h. The influence of learner strategies on oral presentations: a comparison between group and individual performance. Engl Specif Purp. 2011;30(4):272–85.

Harris A, Jones M, Huffman J. Teachers leading educational reform. The power of. 2017.

Agustina L. Stimulating students to speak up through presentation in business English class. J Appl Stud Lang. 2019;3(1):21–8.

Babal JC, Abraham O, Webber S, Watterson T, Moua P, Chen J. Student pharmacist perspectives on factors that influence wellbeing during pharmacy school. Am J Pharm Educ. 2020;84(9):ajpe7831.

Moir F, Yielder J, Sanson J, Chen Y. Depression in medical students: current insights. Adv Med Educ Pract. 2018;323:33.

Pavlinac Dodig I, Lusic Kalcina L, Demirovic S, Pecotic R, Valic M, Dogas Z. Sleep and lifestyle habits of medical and non-medical students during the COVID-19 lockdown. Behav Sci. 2023;13(5):407.

Nowak G, Speed O, Vuk J. Microlearning activities improve student comprehension of difficult concepts and performance in a biochemistry course. Currents Pharm Teach Learn. 2023;15(1):69–78.

Mehmood K, Memon S, Ali F. Language barriers to Effective Communication in speaking English: a phenomenological study of Pakistan International cricketers. Pakistan Lang Humanit Rev. 2024;8(1):107–14.

Buelow JR, Downs D, Jorgensen K, Karges JR, Nelson D. Building interdisciplinary teamwork among allied health students through live clinical case simulations. J Allied Health. 2008;37(2):e109–23.

Rodríguez-Sedano FJ, Conde M, Fernández-Llamas C, editors. Measuring teamwork competence development in a multidisciplinary project based learning environment. Learning and Collaboration Technologies Design, Development and Technological Innovation: 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15–20, 2018, Proceedings, Part I 5; 2018: Springer.

Wilson L, Ho S, Brookes RH. Student perceptions of teamwork within assessment tasks in undergraduate science degrees. Assess Evaluation High Educ. 2018;43(5):786–99.

Parmar D, Bickmore T. Making it personal: addressing individual audience members in oral presentations using augmented reality. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020;4(2):1–22.

Pasandín AMR, Pérez IP, Iglesias PO, Díaz JJG. Cooperative oral presentations in higher education to enhance technical and soft skills in engineering students. Int J Continuing Eng Educ Life Long Learn. 2023;33(6):592–607.

Ragupathi K, Lee A. Beyond fairness and consistency in grading: The role of rubrics in higher education. Diversity and inclusion in global higher education: Lessons from across Asia. 2020:73–95.

Rabbi MF, Islam MS. The effect of academic stress and Mental anxiety among the students of Khulna University. Edukasiana: Jurnal Inovasi Pendidikan. 2024;3(3):280–99.

Popa CO, Schenk A, Rus A, Szasz S, Suciu N, Szabo DA, Cojocaru C. The role of acceptance and planning in stress management for medical students. Acta Marisiensis-Seria Med. 2020;66(3):101–5.

Christianson M, Payne S. Helping students develop skills for better presentations: Using the 20x20 format for presentation training. 語学研究. 2012;26:1–15.

Romero-Yesa S, Fonseca D, Aláez M, Amo-Filva D. Qualitative assessment of a challenge-based learning and teamwork applied in electronics program. Heliyon. 2023;9(12).

Bosco TJ, Gabriel B, Florence M, Gilbert N. Towards effective teaching and learning ESP in mixed classes: students’ interest, challenges and remedies. Int J Engl Literature Social Sci. 2020;5(2):506–16.

Qiao L, Xu Y, bin Ahmad N, An Analysis Of Implementation Challenges For English, For Specific Purposes (Esp) Formative Assessment Via Blended Learning Mode At Chinese Vocational Polytechnics. Journal Of Digital Education, Communication, And Arts (DECA). 2023;6(02):64–76.

Kho MG-W, Ting S-H. Overcoming oral presentation anxiety: a systematic review of Tertiary ESL/EFL Students’ challenges and strategies. Qeios. 2023.

Download references

We confirm that no funding was received for this work.

Author information

Authors and affiliations.

Department of English Language, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

Elham Nasiri & Laleh Khojasteh

You can also search for this author in PubMed   Google Scholar

Contributions

L.KH was involved in writing the proposal, reviewing the text, analyzing the data, and writing the manuscript. E. N was involvedin designing the research and collecting and analyzing the data. Both authors have reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Laleh Khojasteh .

Ethics declarations

Ethics approval and consent to participate.

Our study, entitled “Evaluating Panel Discussions in ESP Classes: An Exploration of International Medical Students’ and ESP Instructors’ Perspectives through Qualitative Research,” was reviewed by the Institutional Review Board (IRB) of the School of Paramedical Sciences, Shiraz University of Medical Sciences (SUMS). The IRB reviewed the study on August 14th, 2024, and determined that formal ethics approval or a reference number was not required. This decision was based on the fact that the research posed minimal risk to participants and focused solely on their educational experiences without involving any intervention or the collection of sensitive personal data.

Consent for publication

Not Applicable.

Competing interests

We confirm that there are no known conflicts of interest associated with this publication and that this work did not receive any financial support.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Nasiri, E., Khojasteh, L. Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research. BMC Med Educ 24 , 925 (2024). https://doi.org/10.1186/s12909-024-05911-3

Download citation

Received : 08 May 2024

Accepted : 14 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1186/s12909-024-05911-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Group oral presentations
• Medical students
• Panel discussions
• ESP courses

BMC Medical Education

ISSN: 1472-6920

• General enquiries: [email protected]

Can rule-based educational chatbots be an acceptable alternative for students in higher education?

• Published: 26 August 2024

Cite this article

• Hakan Güldal   ORCID: orcid.org/0000-0002-1566-9378 1 &
• Emrah Oğuzhan Dinçer   ORCID: orcid.org/0000-0001-7938-3881 2  

35 Accesses

Explore all metrics

The purpose of this study was to investigate student perceptions and acceptance of a rule-based educational chatbot in higher education, employing the TAM (Technology Acceptance Model) framework. The researchers developed a rule-based chatbot for this purpose and examined the students' technology acceptance using qualitative research methods. Therefore, the study was design-based research using qualitative research methods. The participants of the study comprised 22 students studying in the Science Teaching program of Trakya University Faculty of Education and enrolled in the Modern Physics Course in the 2021–2022 fall semester. The research revealed that students' technology acceptance towards rule-based chatbots was high, even though these chatbots had technological limitations when compared to machine learning or deep learning-based ones. The students found rule-based chatbots to be useful, especially in terms of response quality, information quality, and access. Additionally, some technical details and open-source codes were also presented in the study, which can be a guide for rule-based chatbots to be designed for other areas of education.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Examining ChatGPT’s validity as a source for scientific inquiry and its misconceptions regarding cell energy metabolism

Harnessing Rule-Based Chatbots to Support Teaching Python Programming Best Practices

Envisioned Pedagogical Uses of Chatbots in Higher Education and Perceived Benefits and Challenges

Explore related subjects.

• Artificial Intelligence
• Digital Education and Educational Technology

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

https://github.com/hguldal/hermes

Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2 , 100006. https://doi.org/10.1016/j.mlwa.2020.100006

Article   Google Scholar  

Aguilar-Mejía, J. R., & Tejeda, S. (2020). Using virtual assistant for learning selected topics of physics. IEEE integrated STEM education conference (ISEC), 2020 , 1–4. https://doi.org/10.1109/ISEC49744.2020.9397822

Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125 , 389–412. https://doi.org/10.1016/j.compedu.2018.06.008

Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15 (4), ep448.

Alstein, P., Krijtenburg-Lewerissa, K., & van Joolingen, W. R. (2021). Teaching and learning special relativity theory in secondary and lower undergraduate education: A literature review. Phys. Rev. Phys. Educ. Res., 17 , 023101. https://doi.org/10.1103/PhysRevPhysEducRes.17.023101

Anderson, T., & Shattuck, J. (2012). Design-Based Research: A decade of progress in education research? Educational Researcher, 41 (1), 16–25. https://doi.org/10.3102/0013189X11428813

Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54 , 101473. https://doi.org/10.1016/j.tele.2020.101473

Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval . ACM press.

Google Scholar  

Berander, P., Damm, L. O., Eriksson, J., Gorschek, T., Henningsson, K., Jönsson, P., ... & Wohlin, C. (2005). Software quality attributes and trade-offs. Blekinge Institute of Technology, 97(98), 19.

Bhullar, P. S., Joshi, M., & Chugh, R. (2024). ChatGPT in higher education - a synthesis of the literature and a future research agenda. Education and Information Technologies . https://doi.org/10.1007/s10639-024-12723-x

Bilquise, G., Ibrahim, S., & Salhieh, S. M. (2023). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies, 29 (5), 6357–6382. https://doi.org/10.1007/s10639-023-12076-x

Bird, S. & Loper, E. (2004). NLTK: The Natural Language Toolkit. In Proceedings of the ACL Interactive Poster and Demonstration Sessions, pages 214–217, Barcelona, Spain. Association for Computational Linguistics.

Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://doi.org/10.30935/cedtech/13176

Brachten, F., Kissmer, T., & Stieglitz, S. (2021). The acceptance of chatbots in an enterprise context – a survey study. International Journal of Information Management, 60 , 102375. https://doi.org/10.1016/j.ijinfomgt.2021.102375

Cakır, R., & Solak, E. (2015). Attitude of Turkish EFL learners towards e-learning through Tam model. Procedia - Social and Behavioral Sciences, 176 , 596–601. https://doi.org/10.1016/j.sbspro.2015.01.515

Casheekar, A., Lahiri, A., Rath, K., Prabhakar, K. S., & Srinivasan, K. (2024). A contemporary review on chatbots, AI-powered virtual conversational agents, ChatGPT: Applications, open challenges and future research directions. Computer Science Review, 52 , 100632. https://doi.org/10.1016/j.cosrev.2024.100632

Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145 , 552–568. https://doi.org/10.1016/j.jbusres.2022.02.088

Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53 (2), 216–227. https://doi.org/10.1016/j.compedu.2009.01.014

Chun Ho, C., Lee, H. L., Lo, W. K., & Lui, K. F. A. (2018). Developing a Chatbot for College Student Programme Advisement. International Symposium on Educational Technology (ISET), 2018 , 52–56. https://doi.org/10.1109/ISET.2018.00021

Cidral, W. A., Oliveira, T., Di Felice, M., & Aparício, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education; Elsevier BV. https://doi.org/10.1016/j.compedu.2017.12.001

Colby, K. M., Weber, S., & Hilf, F. D. (1971). Artificial paranoia. Artificial Intelligence, 2 (1), 1–25. https://doi.org/10.1016/0004-3702(71)90002-6

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319.

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38 (3), 475–487.

Davis, F. D. (2011). Foreword in technology acceptance in education: Research and issues . Sense Publishers.

Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15 (4), 2940. https://doi.org/10.3390/su15042940

Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26 (5), 6241–6265. https://doi.org/10.1007/s10639-021-10627-8

Dibitonto, M., Leszczynska, K., Tazzi, F., & Medaglia, C. M. (2018). Chatbot in a campus environment: Design of LiSA, a virtual assistant to help students in their university life. In M. Kurosu (Ed.), Human-computer interaction. Interaction technologies (pp. 103–116). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-91250-9_9

Chapter   Google Scholar  

Dimitriadi, K., & Halkia, K. (2012). Secondary students’ understanding of basic ideas of special relativity. International Journal of Science Education, 34 (16), 2565–2582. https://doi.org/10.1080/09500693.2012.705048

Feng, W., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development . https://doi.org/10.1007/bf02504682

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education . McGraw-Hill Education.

Gokcearslan, S., Tosun, C., & Erdemir, Z. G. (2024). Benefits, challenges, and methods of artificial intelligence (AI) chatbots in education: A systematic literature review. International Journal of Technology in Education (IJTE), 7 (1), 19–39. https://doi.org/10.46328/ijte.600

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50 , 2572–2593. https://doi.org/10.1111/bjet.12864

Hew, K. F., Huang, W., Du, J., & Jia, C. (2022). Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions. Journal of Computing in Higher Education; Springer Science+Business Media. https://doi.org/10.1007/s12528-022-09338-x

Hill, J., Randolph Ford, W., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49 , 245–250. https://doi.org/10.1016/j.chb.2015.02.02610.1007/bf02504682

Hu, Y. H. (2021). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies . https://doi.org/10.1007/s10639-021-10664-3

Hwang, G. J., & Chang, C. Y. (2023). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31 (7), 4099–4112. https://doi.org/10.1080/10494820.2021.1952615

Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28 (1), 973–1018. https://doi.org/10.1007/s10639-022-11177-3

Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education . https://doi.org/10.1186/s41239-023-00426-1

Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191 , 104646. https://doi.org/10.1016/j.compedu.2022.104646

Lin, C.-C., Huang, A. Y. Q., & Yang, S. J. H. (2023). A Review of AI-driven conversational chatbots implementation methodologies and challenges (1999–2022). Sustainability, 15 (5), 4012. https://doi.org/10.3390/su15054012 . MDPI AG.

Liu, B., & Mei, C. (2020). Lifelong Knowledge Learning in Rule-based Dialogue Systems. ArXiv, abs/2011.09811. https://doi.org/10.48550/arXiv.2011.09811

Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14 (1), 81–95. https://doi.org/10.1007/s10209-014-0348-1

Marikyan, D. and Papagiannidis, S. (2023) Technology Acceptance Model: A review. In S. Papagiannidis (Ed), TheoryHub Book. Available at http://open.ncl.ac.uk/

Mendoza, S., Sánchez-Adame, L. M., Urquiza-Yllescas, J. F., González-Beltrán, B. A., & Decouchant, D. (2022). A model to develop chatbots for assisting the teaching and learning process. Sensors, 22 (15), 5532. https://doi.org/10.3390/s22155532

Mikic Fonte, F. A., Llamas Nistal, M., Llamas Nistal, M., & Caeiro Rodríguez, M. (2016). NLAST: A natural language assistant for students. IEEE Global Engineering Education Conference (EDUCON), 2016 , 709–713. https://doi.org/10.1109/EDUCON.2016.7474628

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage Publications, Inc.

Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Computers & Education, 61 , 158–167. https://doi.org/10.1016/j.compedu.2012.09.016

Na-young, K., Cha, Y. J., & Kim, H.-S. (2019). Future English Learning: Chatbots and Artificial Intelligence. Multimedia-Assisted Language Learning, 22, 32–53. Retrieved from https://api.semanticscholar.org/CorpusID:209451313

Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2 , 100033. https://doi.org/10.1016/j.caeai.2021.100033

Ondáš, S., Pleva, M., & Hládek, D. (2019). How chatbots can be involved in the education process. 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA), 575–580. https://doi.org/10.1109/ICETA48886.2019.9040095

OpenAI (2023). GPT-4 Technical Report. arXiv.org. https://arxiv.org/abs/2303.08774

Oppermann, R. (2002). User-interface Design. In: Adelsberger, H.H., Collis, B., Pawlowski, J.M. (eds) Handbook on Information Technologies for Education and Training. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https:doi.org/10.1007/978-3-662-07682-8_15

Paul, S., & Ray, L. (2022). Service chatbots and international students: A systematic review. International Conference on Computational Science and Computational Intelligence (CSCI), 2022 , 160–164. https://doi.org/10.1109/CSCI58124.2022.00032

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12 , 2825–2830.

Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28 (6), 1549–1565. https://doi.org/10.1002/cae.22326

Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145 , 103732. https://doi.org/10.1016/j.compedu.2019.103732

Ramesh, K., Ravishankaran, S., Joshi, A., & Chandrasekaran, K. (2017). A Survey of Design Techniques for Conversational Agents. In S. Kaushik, D. Gupta, L. Kharb, & D. Chahal (Eds.), Information, Communication and Computing Technology (pp. 336–350). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-6544-6_31

Rosli, M. S., Saleh, N. S., Md Ali, A., Abu Bakar, S., & Mohd Tahir, L. (2022). A systematic review of the technology acceptance model for the sustainability of higher education during the COVID-19 pandemic and identified research gaps. Sustainability , 14 (18), 11389. https://doi.org/10.3390/su141811389

Ruan, S., Jiang, L., Xu, J., Tham, B. J. K., Qiu, Z., Zhu, Y., Murnane, E. L., Brunskill, E., & Landay, J. A. (2019). QuizBot: A Dialogue-Based Adaptive Learning System for Factual Knowledge. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3290605.3300587

Sætra, H. S. (2023). Generative AI: Here to stay, but for good? Technology in Society . https://doi.org/10.1016/j.techsoc.2023.102372

Santos, R. P. D. (2023). Enhancing Physics Learning with ChatGPT. Bing Chat, and Bard as Agents-to-Think-with: A Comparative Case Study. https://doi.org/10.2139/ssrn.4478305

Scherr, R. E., Shaffer, P. S., & Vokos, S. (2001). Student understanding of time in special relativity: Simultaneity and reference frames. American Journal of Physics, 69 (S1), S24–S35. https://doi.org/10.1119/1.1371254

Shevat, A. (2017). Designing bots: Creating conversational experiences . O’Reilly Media, Inc.

Smutný, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education . https://doi.org/10.1016/j.compedu.2020.103862

Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51 (2), 163–184. https://doi.org/10.2190/EC.51.2.b

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H. T., ..., & Le, Q. (2022). Lamda: Language models for dialog applications . arXiv preprint arXiv:2201.08239

Turing, A. (1950). Computing Machinery and Intelligence. Mind, 59 (236), 433–460.

Article   MathSciNet   Google Scholar  

Vakarou, G., Stylos, G., & Kotsis, K. T. (2024). Probing students’ understanding of Einsteinian physics concepts: A study in primary and secondary Greek schools. Physics Education, 59 (2), 025004. https://doi.org/10.1088/1361-6552/ad1768

Valtolina, S., Barricelli, B. R., & Di Gaetano, S. (2020). Communicability of traditional interfaces VS chatbots in healthcare and smart home domains. Behaviour & Information Technology, 39 (1), 108–132. https://doi.org/10.1080/0144929X.2019.1637025

Vanichvasin, P. (2021). Chatbot development as a digital learning tool to increase students’ research knowledge. International Education Studies; Canadian Center of Science and Education . https://doi.org/10.5539/ies.v14n2p44

Wallace, R. S. (2009). The anatomy of A.L.I.C.E. In: R. Epstein, G. Roberts, G. Beber, (Eds.), Parsing the turing test . Springer. https://doi.org/10.1007/978-1-4020-6710-5_13

Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53 (4), 5–23.

Weizenbaum, J. (1966). ELIZA: A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9 (1), 36–45.

Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are We There Yet? - A Systematic Literature Review on Chatbots in Education. Frontiers in Artificial Intelligence, 4 , 654924. https://doi.org/10.3389/frai.2021.654924

Wu, R., & Yu, Z. (2024). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55 , 10–33. https://doi.org/10.1111/bjet.13334

Zhang, R., Zou, D., & Cheng, G. (2023). A review of chatbot-assisted learning: Pedagogical approaches, implementations, factors leading to effectiveness, theories, and future directions. Interactive Learning Environments . https://doi.org/10.1080/10494820.2023.2202704

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., ... Wen, J. R. (2023). A survey of large language models. Retrieved January 9, 2024, from arXiv.org. https://arxiv.org/abs/2303.18223

Download references

Acknowledgements

Not applicable.

The authors confirm that they have no funding for this study.

Author information

Authors and affiliations.

Faculty of Education, Department of Computer and Instructional Technologies Edirne, Trakya University, Edirne, Türkiye

Hakan Güldal

Faculty of Education, Department of Mathematics and Science Education Edirne, Trakya University, Edirne, Türkiye

Emrah Oğuzhan Dinçer

You can also search for this author in PubMed   Google Scholar

Contributions

Hakan Güldal: Conceptualization, Software, Methodology, Validation, Visualization, Writing-Original draft preparation, Writing- Reviewing and Editing. Emrah Oğuzhan Dinçer: Methodology, Validation, Writing-Original draft preparation, Writing- Reviewing and Editing.

Corresponding author

Correspondence to Hakan Güldal .

Ethics declarations

Ethics statement.

Approval was received from Trakya University's Ethics Committee of Social Sciences for the study (E-29563864–050.04.04–162428).

Competing interests

The authors confirm no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Güldal, H., Dinçer, E.O. Can rule-based educational chatbots be an acceptable alternative for students in higher education?. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12977-5

Download citation

Received : 19 February 2024

Accepted : 09 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1007/s10639-024-12977-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Rule-Based Chatbot
• Machine learning
• Artificial intelligence
• Find a journal
• Publish with us
• Track your research

23 Advantages and Disadvantages of Qualitative Research

Investigating methodologies. Taking a closer look at ethnographic, anthropological, or naturalistic techniques. Data mining through observer recordings. This is what the world of qualitative research is all about. It is the comprehensive and complete data that is collected by having the courage to ask an open-ended question.

Print media has used the principles of qualitative research for generations. Now more industries are seeing the advantages that come from the extra data that is received by asking more than a “yes” or “no” question.

The advantages and disadvantages of qualitative research are quite unique. On one hand, you have the perspective of the data that is being collected. On the other hand, you have the techniques of the data collector and their own unique observations that can alter the information in subtle ways.

That’s why these key points are so important to consider.

What Are the Advantages of Qualitative Research?

1. Subject materials can be evaluated with greater detail. There are many time restrictions that are placed on research methods. The goal of a time restriction is to create a measurable outcome so that metrics can be in place. Qualitative research focuses less on the metrics of the data that is being collected and more on the subtleties of what can be found in that information. This allows for the data to have an enhanced level of detail to it, which can provide more opportunities to glean insights from it during examination.

2. Research frameworks can be fluid and based on incoming or available data. Many research opportunities must follow a specific pattern of questioning, data collection, and information reporting. Qualitative research offers a different approach. It can adapt to the quality of information that is being gathered. If the available data does not seem to be providing any results, the research can immediately shift gears and seek to gather data in a new direction. This offers more opportunities to gather important clues about any subject instead of being confined to a limited and often self-fulfilling perspective.

3. Qualitative research data is based on human experiences and observations. Humans have two very different operating systems. One is a subconscious method of operation, which is the fast and instinctual observations that are made when data is present. The other operating system is slower and more methodical, wanting to evaluate all sources of data before deciding. Many forms of research rely on the second operating system while ignoring the instinctual nature of the human mind. Qualitative research doesn’t ignore the gut instinct. It embraces it and the data that can be collected is often better for it.

4. Gathered data has a predictive quality to it. One of the common mistakes that occurs with qualitative research is an assumption that a personal perspective can be extrapolated into a group perspective. This is only possible when individuals grow up in similar circumstances, have similar perspectives about the world, and operate with similar goals. When these groups can be identified, however, the gathered individualistic data can have a predictive quality for those who are in a like-minded group. At the very least, the data has a predictive quality for the individual from whom it was gathered.

5. Qualitative research operates within structures that are fluid. Because the data being gathered through this type of research is based on observations and experiences, an experienced researcher can follow-up interesting answers with additional questions. Unlike other forms of research that require a specific framework with zero deviation, researchers can follow any data tangent which makes itself known and enhance the overall database of information that is being collected.

6. Data complexities can be incorporated into generated conclusions. Although our modern world tends to prefer statistics and verifiable facts, we cannot simply remove the human experience from the equation. Different people will have remarkably different perceptions about any statistic, fact, or event. This is because our unique experiences generate a different perspective of the data that we see. These complexities, when gathered into a singular database, can generate conclusions with more depth and accuracy, which benefits everyone.

7. Qualitative research is an open-ended process. When a researcher is properly prepared, the open-ended structures of qualitative research make it possible to get underneath superficial responses and rational thoughts to gather information from an individual’s emotional response. This is critically important to this form of researcher because it is an emotional response which often drives a person’s decisions or influences their behavior.

8. Creativity becomes a desirable quality within qualitative research. It can be difficult to analyze data that is obtained from individual sources because many people subconsciously answer in a way that they think someone wants. This desire to “please” another reduces the accuracy of the data and suppresses individual creativity. By embracing the qualitative research method, it becomes possible to encourage respondent creativity, allowing people to express themselves with authenticity. In return, the data collected becomes more accurate and can lead to predictable outcomes.

9. Qualitative research can create industry-specific insights. Brands and businesses today need to build relationships with their core demographics to survive. The terminology, vocabulary, and jargon that consumers use when looking at products or services is just as important as the reputation of the brand that is offering them. If consumers are receiving one context, but the intention of the brand is a different context, then the miscommunication can artificially restrict sales opportunities. Qualitative research gives brands access to these insights so they can accurately communicate their value propositions.

10. Smaller sample sizes are used in qualitative research, which can save on costs. Many qualitative research projects can be completed quickly and on a limited budget because they typically use smaller sample sizes that other research methods. This allows for faster results to be obtained so that projects can move forward with confidence that only good data is able to provide.

11. Qualitative research provides more content for creatives and marketing teams. When your job involves marketing, or creating new campaigns that target a specific demographic, then knowing what makes those people can be quite challenging. By going through the qualitative research approach, it becomes possible to congregate authentic ideas that can be used for marketing and other creative purposes. This makes communication between the two parties to be handled with more accuracy, leading to greater level of happiness for all parties involved.

12. Attitude explanations become possible with qualitative research. Consumer patterns can change on a dime sometimes, leaving a brand out in the cold as to what just happened. Qualitative research allows for a greater understanding of consumer attitudes, providing an explanation for events that occur outside of the predictive matrix that was developed through previous research. This allows the optimal brand/consumer relationship to be maintained.

What Are the Disadvantages of Qualitative Research?

1. The quality of the data gathered in qualitative research is highly subjective. This is where the personal nature of data gathering in qualitative research can also be a negative component of the process. What one researcher might feel is important and necessary to gather can be data that another researcher feels is pointless and won’t spend time pursuing it. Having individual perspectives and including instinctual decisions can lead to incredibly detailed data. It can also lead to data that is generalized or even inaccurate because of its reliance on researcher subjectivisms.

2. Data rigidity is more difficult to assess and demonstrate. Because individual perspectives are often the foundation of the data that is gathered in qualitative research, it is more difficult to prove that there is rigidity in the information that is collective. The human mind tends to remember things in the way it wants to remember them. That is why memories are often looked at fondly, even if the actual events that occurred may have been somewhat disturbing at the time. This innate desire to look at the good in things makes it difficult for researchers to demonstrate data validity.

3. Mining data gathered by qualitative research can be time consuming. The number of details that are often collected while performing qualitative research are often overwhelming. Sorting through that data to pull out the key points can be a time-consuming effort. It is also a subjective effort because what one researcher feels is important may not be pulled out by another researcher. Unless there are some standards in place that cannot be overridden, data mining through a massive number of details can almost be more trouble than it is worth in some instances.

4. Qualitative research creates findings that are valuable, but difficult to present. Presenting the findings which come out of qualitative research is a bit like listening to an interview on CNN. The interviewer will ask a question to the interviewee, but the goal is to receive an answer that will help present a database which presents a specific outcome to the viewer. The goal might be to have a viewer watch an interview and think, “That’s terrible. We need to pass a law to change that.” The subjective nature of the information, however, can cause the viewer to think, “That’s wonderful. Let’s keep things the way they are right now.” That is why findings from qualitative research are difficult to present. What a research gleans from the data can be very different from what an outside observer gleans from the data.

5. Data created through qualitative research is not always accepted. Because of the subjective nature of the data that is collected in qualitative research, findings are not always accepted by the scientific community. A second independent qualitative research effort which can produce similar findings is often necessary to begin the process of community acceptance.

6. Researcher influence can have a negative effect on the collected data. The quality of the data that is collected through qualitative research is highly dependent on the skills and observation of the researcher. If a researcher has a biased point of view, then their perspective will be included with the data collected and influence the outcome. There must be controls in place to help remove the potential for bias so the data collected can be reviewed with integrity. Otherwise, it would be possible for a researcher to make any claim and then use their bias through qualitative research to prove their point.

7. Replicating results can be very difficult with qualitative research. The scientific community wants to see results that can be verified and duplicated to accept research as factual. In the world of qualitative research, this can be very difficult to accomplish. Not only do you have the variability of researcher bias for which to account within the data, but there is also the informational bias that is built into the data itself from the provider. This means the scope of data gathering can be extremely limited, even if the structure of gathering information is fluid, because of each unique perspective.

8. Difficult decisions may require repetitive qualitative research periods. The smaller sample sizes of qualitative research may be an advantage, but they can also be a disadvantage for brands and businesses which are facing a difficult or potentially controversial decision. A small sample is not always representative of a larger population demographic, even if there are deep similarities with the individuals involve. This means a follow-up with a larger quantitative sample may be necessary so that data points can be tracked with more accuracy, allowing for a better overall decision to be made.

9. Unseen data can disappear during the qualitative research process. The amount of trust that is placed on the researcher to gather, and then draw together, the unseen data that is offered by a provider is enormous. The research is dependent upon the skill of the researcher being able to connect all the dots. If the researcher can do this, then the data can be meaningful and help brands and progress forward with their mission. If not, there is no way to alter course until after the first results are received. Then a new qualitative process must begin.

10. Researchers must have industry-related expertise. You can have an excellent researcher on-board for a project, but if they are not familiar with the subject matter, they will have a difficult time gathering accurate data. For qualitative research to be accurate, the interviewer involved must have specific skills, experiences, and expertise in the subject matter being studied. They must also be familiar with the material being evaluated and have the knowledge to interpret responses that are received. If any piece of this skill set is missing, the quality of the data being gathered can be open to interpretation.

11. Qualitative research is not statistically representative. The one disadvantage of qualitative research which is always present is its lack of statistical representation. It is a perspective-based method of research only, which means the responses given are not measured. Comparisons can be made and this can lead toward the duplication which may be required, but for the most part, quantitative data is required for circumstances which need statistical representation and that is not part of the qualitative research process.

The advantages and disadvantages of qualitative research make it possible to gather and analyze individualistic data on deeper levels. This makes it possible to gain new insights into consumer thoughts, demographic behavioral patterns, and emotional reasoning processes. When a research can connect the dots of each information point that is gathered, the information can lead to personalized experiences, better value in products and services, and ongoing brand development.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved August 27, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• Current issue
• Write for Us
• BMJ Journals

You are here

• Volume 18, Issue 2
• Issues of validity and reliability in qualitative research
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• Helen Noble 1 ,
• Joanna Smith 2
• 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• 2 School of Human and Health Sciences, University of Huddersfield , Huddersfield , UK
• Correspondence to Dr Helen Noble School of Nursing and Midwifery, Queens's University Belfast, Medical Biology Centre, 97 Lisburn Rd, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2015-102054

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery. In a previous article we explored ‘bias’ across research designs and outlined strategies to minimise bias. 1 The aim of this article is to further outline rigour, or the integrity in which a study is conducted, and ensure the credibility of findings in relation to qualitative research. Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research. In addition, some of the strategies adopted by qualitative researchers to enhance the credibility of their research are outlined.

Are the terms reliability and validity relevant to ensuring credibility in qualitative research?

Although the tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evaluate qualitative research. 2–4 In the broadest context these terms are applicable, with validity referring to the integrity and application of the methods undertaken and the precision in which the findings accurately reflect the data, while reliability describes consistency within the employed analytical procedures. 4 However, if qualitative methods are inherently different from quantitative methods in terms of philosophical positions and purpose, then alterative frameworks for establishing rigour are appropriate. 3 Lincoln and Guba 5 offer alternative criteria for demonstrating rigour within qualitative research namely truth value, consistency and neutrality and applicability. Table 1 outlines the differences in terminology and criteria used to evaluate qualitative research.

• View inline

Terminology and criteria used to evaluate the credibility of research findings

What strategies can qualitative researchers adopt to ensure the credibility of the study findings?

Unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the ‘trustworthiness’ of the findings. Such strategies include:

Accounting for personal biases which may have influenced findings; 6

Acknowledging biases in sampling and ongoing critical reflection of methods to ensure sufficient depth and relevance of data collection and analysis; 3

Meticulous record keeping, demonstrating a clear decision trail and ensuring interpretations of data are consistent and transparent; 3 , 4

Establishing a comparison case/seeking out similarities and differences across accounts to ensure different perspectives are represented; 6 , 7

Including rich and thick verbatim descriptions of participants’ accounts to support findings; 7

Demonstrating clarity in terms of thought processes during data analysis and subsequent interpretations 3 ;

Engaging with other researchers to reduce research bias; 3

Respondent validation: includes inviting participants to comment on the interview transcript and whether the final themes and concepts created adequately reflect the phenomena being investigated; 4

Data triangulation, 3 , 4 whereby different methods and perspectives help produce a more comprehensive set of findings. 8 , 9

Table 2 provides some specific examples of how some of these strategies were utilised to ensure rigour in a study that explored the impact of being a family carer to patients with stage 5 chronic kidney disease managed without dialysis. 10

Strategies for enhancing the credibility of qualitative research

In summary, it is imperative that all qualitative researchers incorporate strategies to enhance the credibility of a study during research design and implementation. Although there is no universally accepted terminology and criteria used to evaluate qualitative research, we have briefly outlined some of the strategies that can enhance the credibility of study findings.

• Sandelowski M
• Lincoln YS ,
• Barrett M ,
• Mayan M , et al
• Greenhalgh T
• Lingard L ,

Twitter Follow Joanna Smith at @josmith175 and Helen Noble at @helnoble

Competing interests None.

Read the full text or download the PDF:

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Neurol Res Pract

How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Privacy Policy

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

One-to-One Interview – Methods and Guide

Questionnaire – Definition, Types, and Examples

Explanatory Research – Types, Methods, Guide

Triangulation in Research – Types, Methods and...

Mixed Methods Research – Types & Analysis

Correlational Research – Methods, Types and...

CRO Platform

Test your insights. Run experiments. Win. Or learn. And then win.

eCommerce Customer Analytics Platform

Acquisition matters. But retention matters more. Understand, monitor & nurture the best customers.

• Case Studies
• Ebooks, Tools, Templates
• Digital Marketing Glossary
• eCommerce Growth Stories
• eCommerce Growth Show
• Help & Technical Documentation

CRO Guide   >  Chapter 3.1

Qualitative Research Methods: Examples, Limitations & Analysis

Qualitative research is a method focused on understanding human behavior and experiences through non-numerical data. Qualitative research methods include:

• One-on-one interviews,
• Focus groups, Ethnographic research,
• Case studies,
• Record keeping,
• Qualitative observations

In this article, we’ll provide tips and tricks on how to use qualitative research to better understand your audience through real world examples and improve your ROI. We’ll also learn the difference between qualitative and quantitative data.

Table of Contents

Marketers often seek to understand their customers deeply. Qualitative research methods such as face-to-face interviews, focus groups, and qualitative observations can provide valuable insights into your products, your market, and your customers’ opinions and motivations. Understanding these nuances can significantly enhance marketing strategies and overall customer satisfaction.

What is Qualitative Research

Qualitative research is a market research method that focuses on obtaining data through open-ended and conversational communication. This method focuses on the “why” rather than the “what” people think about you. Thus, qualitative research seeks to uncover the underlying motivations, attitudes, and beliefs that drive people’s actions. 

Let’s say you have an online shop catering to a general audience. You do a demographic analysis and you find out that most of your customers are male. Naturally, you will want to find out why women are not buying from you. And that’s what qualitative research will help you find out.

In the case of your online shop, qualitative research would involve reaching out to female non-customers through methods such as in-depth interviews or focus groups. These interactions provide a platform for women to express their thoughts, feelings, and concerns regarding your products or brand. Through qualitative analysis, you can uncover valuable insights into factors such as product preferences, user experience, brand perception, and barriers to purchase.

Types of Qualitative Research Methods

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience regarding a particular topic.

The most frequently used qualitative analysis methods are one-on-one interviews, focus groups, ethnographic research, case study research, record keeping, and qualitative observation.

1. One-on-one interviews

Conducting one-on-one interviews is one of the most common qualitative research methods. One of the advantages of this method is that it provides a great opportunity to gather precise data about what people think and their motivations.

Spending time talking to customers not only helps marketers understand who their clients are, but also helps with customer care: clients love hearing from brands. This strengthens the relationship between a brand and its clients and paves the way for customer testimonials.

• A company might conduct interviews to understand why a product failed to meet sales expectations.
• A researcher might use interviews to gather personal stories about experiences with healthcare.

These interviews can be performed face-to-face or on the phone and usually last between half an hour to over two hours. 

When a one-on-one interview is conducted face-to-face, it also gives the marketer the opportunity to read the body language of the respondent and match the responses.

2. Focus groups

Focus groups gather a small number of people to discuss and provide feedback on a particular subject. The ideal size of a focus group is usually between five and eight participants. The size of focus groups should reflect the participants’ familiarity with the topic. For less important topics or when participants have little experience, a group of 10 can be effective. For more critical topics or when participants are more knowledgeable, a smaller group of five to six is preferable for deeper discussions.

The main goal of a focus group is to find answers to the “why”, “what”, and “how” questions. This method is highly effective in exploring people’s feelings and ideas in a social setting, where group dynamics can bring out insights that might not emerge in one-on-one situations.

• A focus group could be used to test reactions to a new product concept.
• Marketers might use focus groups to see how different demographic groups react to an advertising campaign.

One advantage that focus groups have is that the marketer doesn’t necessarily have to interact with the group in person. Nowadays focus groups can be sent as online qualitative surveys on various devices.

Focus groups are an expensive option compared to the other qualitative research methods, which is why they are typically used to explain complex processes.

3. Ethnographic research

Ethnographic research is the most in-depth observational method that studies individuals in their naturally occurring environment.

This method aims at understanding the cultures, challenges, motivations, and settings that occur.

• A study of workplace culture within a tech startup.
• Observational research in a remote village to understand local traditions.

Ethnographic research requires the marketer to adapt to the target audiences’ environments (a different organization, a different city, or even a remote location), which is why geographical constraints can be an issue while collecting data.

This type of research can last from a few days to a few years. It’s challenging and time-consuming and solely depends on the expertise of the marketer to be able to analyze, observe, and infer the data.

4. Case study research

The case study method has grown into a valuable qualitative research method. This type of research method is usually used in education or social sciences. It involves a comprehensive examination of a single instance or event, providing detailed insights into complex issues in real-life contexts.  

• Analyzing a single school’s innovative teaching method.
• A detailed study of a patient’s medical treatment over several years.

Case study research may seem difficult to operate, but it’s actually one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

Record keeping is similar to going to the library: you go over books or any other reference material to collect relevant data. This method uses already existing reliable documents and similar sources of information as a data source.

• Historical research using old newspapers and letters.
• A study on policy changes over the years by examining government records.

This method is useful for constructing a historical context around a research topic or verifying other findings with documented evidence.

6. Qualitative observation

Qualitative observation is a method that uses subjective methodologies to gather systematic information or data. This method deals with the five major sensory organs and their functioning, sight, smell, touch, taste, and hearing.

• Sight : Observing the way customers visually interact with product displays in a store to understand their browsing behaviors and preferences.
• Smell : Noting reactions of consumers to different scents in a fragrance shop to study the impact of olfactory elements on product preference.
• Touch : Watching how individuals interact with different materials in a clothing store to assess the importance of texture in fabric selection.
• Taste : Evaluating reactions of participants in a taste test to identify flavor profiles that appeal to different demographic groups.
• Hearing : Documenting responses to changes in background music within a retail environment to determine its effect on shopping behavior and mood.

Below we are also providing real-life examples of qualitative research that demonstrate practical applications across various contexts:

Qualitative Research Real World Examples

Let’s explore some examples of how qualitative research can be applied in different contexts.

1. Online grocery shop with a predominantly male audience

Method used: one-on-one interviews.

Let’s go back to one of the previous examples. You have an online grocery shop. By nature, it addresses a general audience, but after you do a demographic analysis you find out that most of your customers are male.

One good method to determine why women are not buying from you is to hold one-on-one interviews with potential customers in the category.

Interviewing a sample of potential female customers should reveal why they don’t find your store appealing. The reasons could range from not stocking enough products for women to perhaps the store’s emphasis on heavy-duty tools and automotive products, for example. These insights can guide adjustments in inventory and marketing strategies.

2. Software company launching a new product

Method used: focus groups.

Focus groups are great for establishing product-market fit.

Let’s assume you are a software company that wants to launch a new product and you hold a focus group with 12 people. Although getting their feedback regarding users’ experience with the product is a good thing, this sample is too small to define how the entire market will react to your product.

So what you can do instead is holding multiple focus groups in 20 different geographic regions. Each region should be hosting a group of 12 for each market segment; you can even segment your audience based on age. This would be a better way to establish credibility in the feedback you receive.

3. Alan Pushkin’s “God’s Choice: The Total World of a Fundamentalist Christian School”

Method used: ethnographic research.

Moving from a fictional example to a real-life one, let’s analyze Alan Peshkin’s 1986 book “God’s Choice: The Total World of a Fundamentalist Christian School”.

Peshkin studied the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community alike, and spending eighteen months observing them to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

The study highlights the school’s unified purpose, rigorous academic environment, and strong community support while also pointing out its lack of cultural diversity and openness to differing viewpoints. These insights are crucial for understanding how such educational settings operate and what they offer to students.

Even after discovering all this, Peshkin still presented the school in a positive light and stated that public schools have much to learn from such schools.

Peshkin’s in-depth research represents a qualitative study that uses observations and unstructured interviews, without any assumptions or hypotheses. He utilizes descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other Christian schools.

4. Understanding buyers’ trends

Method used: record keeping.

Another way marketers can use quality research is to understand buyers’ trends. To do this, marketers need to look at historical data for both their company and their industry and identify where buyers are purchasing items in higher volumes.

For example, electronics distributors know that the holiday season is a peak market for sales while life insurance agents find that spring and summer wedding months are good seasons for targeting new clients.

5. Determining products/services missing from the market

Conducting your own research isn’t always necessary. If there are significant breakthroughs in your industry, you can use industry data and adapt it to your marketing needs.

The influx of hacking and hijacking of cloud-based information has made Internet security a topic of many industry reports lately. A software company could use these reports to better understand the problems its clients are facing.

As a result, the company can provide solutions prospects already know they need.

Real-time Customer Lifetime Value (CLV) Benchmark Report

See where your business stands compared to 1,000+ e-stores in different industries.

Qualitative Research Approaches

Once the marketer has decided that their research questions will provide data that is qualitative in nature, the next step is to choose the appropriate qualitative approach.

The approach chosen will take into account the purpose of the research, the role of the researcher, the data collected, the method of data analysis , and how the results will be presented. The most common approaches include:

• Narrative : This method focuses on individual life stories to understand personal experiences and journeys. It examines how people structure their stories and the themes within them to explore human existence. For example, a narrative study might look at cancer survivors to understand their resilience and coping strategies.
• Phenomenology : attempts to understand or explain life experiences or phenomena; It aims to reveal the depth of human consciousness and perception, such as by studying the daily lives of those with chronic illnesses.
• Grounded theory : investigates the process, action, or interaction with the goal of developing a theory “grounded” in observations and empirical data. 
• Ethnography : describes and interprets an ethnic, cultural, or social group;
• Case study : examines episodic events in a definable framework, develops in-depth analyses of single or multiple cases, and generally explains “how”. An example might be studying a community health program to evaluate its success and impact.

How to Analyze Qualitative Data

Analyzing qualitative data involves interpreting non-numerical data to uncover patterns, themes, and deeper insights. This process is typically more subjective and requires a systematic approach to ensure reliability and validity. 

1. Data Collection

Ensure that your data collection methods (e.g., interviews, focus groups, observations) are well-documented and comprehensive. This step is crucial because the quality and depth of the data collected will significantly influence the analysis.

2. Data Preparation

Once collected, the data needs to be organized. Transcribe audio and video recordings, and gather all notes and documents. Ensure that all data is anonymized to protect participant confidentiality where necessary.

3. Familiarization

Immerse yourself in the data by reading through the materials multiple times. This helps you get a general sense of the information and begin identifying patterns or recurring themes.

Develop a coding system to tag data with labels that summarize and account for each piece of information. Codes can be words, phrases, or acronyms that represent how these segments relate to your research questions.

• Descriptive Coding : Summarize the primary topic of the data.
• In Vivo Coding : Use language and terms used by the participants themselves.
• Process Coding : Use gerunds (“-ing” words) to label the processes at play.
• Emotion Coding : Identify and record the emotions conveyed or experienced.

5. Thematic Development

Group codes into themes that represent larger patterns in the data. These themes should relate directly to the research questions and form a coherent narrative about the findings.

6. Interpreting the Data

Interpret the data by constructing a logical narrative. This involves piecing together the themes to explain larger insights about the data. Link the results back to your research objectives and existing literature to bolster your interpretations.

7. Validation

Check the reliability and validity of your findings by reviewing if the interpretations are supported by the data. This may involve revisiting the data multiple times or discussing the findings with colleagues or participants for validation.

8. Reporting

Finally, present the findings in a clear and organized manner. Use direct quotes and detailed descriptions to illustrate the themes and insights. The report should communicate the narrative you’ve built from your data, clearly linking your findings to your research questions.

Limitations of qualitative research

The disadvantages of qualitative research are quite unique. The techniques of the data collector and their own unique observations can alter the information in subtle ways. That being said, these are the qualitative research’s limitations:

1. It’s a time-consuming process

The main drawback of qualitative study is that the process is time-consuming. Another problem is that the interpretations are limited. Personal experience and knowledge influence observations and conclusions.

Thus, qualitative research might take several weeks or months. Also, since this process delves into personal interaction for data collection, discussions often tend to deviate from the main issue to be studied.

2. You can’t verify the results of qualitative research

Because qualitative research is open-ended, participants have more control over the content of the data collected. So the marketer is not able to verify the results objectively against the scenarios stated by the respondents. For example, in a focus group discussing a new product, participants might express their feelings about the design and functionality. However, these opinions are influenced by individual tastes and experiences, making it difficult to ascertain a universally applicable conclusion from these discussions.

3. It’s a labor-intensive approach

Qualitative research requires a labor-intensive analysis process such as categorization, recording, etc. Similarly, qualitative research requires well-experienced marketers to obtain the needed data from a group of respondents.

4. It’s difficult to investigate causality

Qualitative research requires thoughtful planning to ensure the obtained results are accurate. There is no way to analyze qualitative data mathematically. This type of research is based more on opinion and judgment rather than results. Because all qualitative studies are unique they are difficult to replicate.

5. Qualitative research is not statistically representative

Because qualitative research is a perspective-based method of research, the responses given are not measured.

Comparisons can be made and this can lead toward duplication, but for the most part, quantitative data is required for circumstances that need statistical representation and that is not part of the qualitative research process.

While doing a qualitative study, it’s important to cross-reference the data obtained with the quantitative data. By continuously surveying prospects and customers marketers can build a stronger database of useful information.

Quantitative vs. Qualitative Research

Image source

Quantitative and qualitative research are two distinct methodologies used in the field of market research, each offering unique insights and approaches to understanding consumer behavior and preferences.

As we already defined, qualitative analysis seeks to explore the deeper meanings, perceptions, and motivations behind human behavior through non-numerical data. On the other hand, quantitative research focuses on collecting and analyzing numerical data to identify patterns, trends, and statistical relationships.  

Let’s explore their key differences: 

Nature of Data:

• Quantitative research : Involves numerical data that can be measured and analyzed statistically.
• Qualitative research : Focuses on non-numerical data, such as words, images, and observations, to capture subjective experiences and meanings.

Research Questions:

• Quantitative research : Typically addresses questions related to “how many,” “how much,” or “to what extent,” aiming to quantify relationships and patterns.
• Qualitative research: Explores questions related to “why” and “how,” aiming to understand the underlying motivations, beliefs, and perceptions of individuals.

Data Collection Methods:

• Quantitative research : Relies on structured surveys, experiments, or observations with predefined variables and measures.
• Qualitative research : Utilizes open-ended interviews, focus groups, participant observations, and textual analysis to gather rich, contextually nuanced data.

Analysis Techniques:

• Quantitative research: Involves statistical analysis to identify correlations, associations, or differences between variables.
• Qualitative research: Employs thematic analysis, coding, and interpretation to uncover patterns, themes, and insights within qualitative data.

Do Conversion Rate Optimization the Right way.

Explore helps you make the most out of your CRO efforts through advanced A/B testing, surveys, advanced segmentation and optimised customer journeys.

If you haven’t subscribed yet to our newsletter, now is your chance!

Like what you’re reading?

Join the informed ecommerce crowd.

We will never bug you with irrelevant info.

By clicking the Button, you confirm that you agree with our Terms and Conditions .

Continue your Conversion Rate Optimization Journey

• Last modified: January 3, 2023
• Conversion Rate Optimization , User Research

Valentin Radu

We’re a team of people that want to empower marketers around the world to create marketing campaigns that matter to consumers in a smart way. Meet us at the intersection of creativity, integrity, and development, and let us show you how to optimize your marketing.

Our Software

• > Book a Demo
• > Partner Program
• > Affiliate Program
• Blog Sitemap

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

• Business Intelligence Reporting
• Data Driven
• Data Analysis Method
• Business Model
• Business Analysis
• Quantitative Research
• Business Analytics
• Marketing Analytics
• Data Integration
• Digital Transformation Strategy
• Online Training
• Local Training Events

5 Strengths and 5 Limitations of Qualitative Research

Lauren Christiansen

Insight into qualitative research.

Anyone who reviews a bunch of numbers knows how impersonal that feels. What do numbers really reveal about a person's beliefs, motives, and thoughts? While it's critical to collect statistical information to identify business trends and inefficiencies, stats don't always tell the full story. Why does the customer like this product more than the other one? What motivates them to post this particular hashtag on social media? How do employees actually feel about the new supply chain process? To answer more personal questions that delve into the human experience, businesses often employ a qualitative research process.

10 Key Strengths and Limitations of Qualitative Research

Qualitative research helps entrepreneurs and established companies understand the many factors that drive consumer behavior. Because most organizations collect and analyze quantitative data, they don't always know exactly how a target market feels and what it wants. It helps researchers when they can observe a small sample size of consumers in a comfortable environment, ask questions, and let them speak. Research methodology varies depending on the industry and type of business needs. Many companies employ mixed methods to extract the insights they require to improve decision-making. While both quantitative research and qualitative methods are effective, there are limitations to both. Quantitative research is expensive, time-consuming, and presents a limited understanding of consumer needs. However, qualitative research methods generate less verifiable information as all qualitative data is based on experience. Businesses should use a combination of both methods to overcome any associated limitations.

Strengths of Qualitative Research

• Captures New Beliefs - Qualitative research methods extrapolate any evolving beliefs within a market. This may include who buys a product/service, or how employees feel about their employers.
• Fewer Limitations - Qualitative studies are less stringent than quantitative ones. Outside the box answers to questions, opinions, and beliefs are included in data collection and data analysis.
• More Versatile - Qualitative research is much easier at times for researchers. They can adjust questions, adapt to circumstances that change or change the environment to optimize results.
• Greater Speculation - Researchers can speculate more on what answers to drill down into and how to approach them. They can use instinct and subjective experience to identify and extract good data.
• More Targeted - This research process can target any area of the business or concern it may have. Researchers can concentrate on specific target markets to collect valuable information. This takes less time and requires fewer resources than quantitative studies.

Limitations of Qualitative Research

• Sample Sizes - Businesses need to find a big enough group of participants to ensure results are accurate. A sample size of 15 people is not enough to show a reliable picture of how consumers view a product. If it is not possible to find a large enough sample size, the data collected may be insufficient.
• Bias - For internal qualitative studies, employees may be biased. For example, workers may give a popular answer that colleagues agree with rather than a true opinion. This can negatively influence the outcome of the study.
• Self-Selection Bias - Businesses that call on volunteers to answer questions worry that the people who respond are not reflective of the greater group. It is better if the company selects individuals at random for research studies, particularly if they are employees. However, this changes the process from qualitative to quantitative methods.
• Artificial - It isn't typical to observe consumers in stores, gather a focus group together, or ask employees about their experiences at work. This artificiality may impact the findings, as it is outside the norm of regular behavior and interactions.
• Quality - Questions It's hard to know whether researcher questions are quality or not because they are all subjective. Researchers need to ask how and why individuals feel the way they do to receive the most accurate answers.

Key Takeaways on Strengths and Limitations of Qualitative Research

• Qualitative research helps entrepreneurs and small businesses understand what drives human behavior. It is also used to see how employees feel about workflows and tasks.
• Companies can extract insights from qualitative research to optimize decision-making and improve products or services.
• Qualitative research captures new beliefs, has fewer limitations, is more versatile, and is more targeted. It also allows researchers to speculate and insert themselves more into the research study.
• Qualitative research has many limitations which include possible small sample sizes, potential bias in answers, self-selection bias, and potentially poor questions from researchers. It also can be artificial because it isn't typical to observe participants in focus groups, ask them questions at work, or invite them to partake in this type of research method.

Must-Read Content

The Top Qualitative Research Methods for Business Success

5 Qualitative Research Examples in Action

7 Types of Qualitative Research to Look Out For

What is Qualitative Research, Really?